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Synthese

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Zusammenfassung

We know a lot about the phenomena involved in the use of our techniques. Some of what we know has been learned deductively, using assumptions and mathematics. We do learn from practice, as well as from deduction and from experimental sampling. We can practice a science. We need not hide behind a mysterious shield of false-tofact deduction!

Now these two directions, the one active the other contemplative, are one and the same thing; and what in operation is most useful, that in knowledge is most true.

(Bacon 1620: Buch 2, Aphorismus 4, letzter Satz)

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Literatur

  • Akaike, H. (1973). Information Theory and an Extension of the Maximum Likelihood Principle. In: Petrov, B.N.; and Csàki, F. (Hrsg.) Second International Symposium on Information Theory Akademiai Kiàdo, Budapest. Wiederabgedruckt in Kotz und Johnson (1993: Bd. I, 610-624).

    Google Scholar 

  • Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control 19(6), 716–723.

    MathSciNet  MATH  Google Scholar 

  • Akaike, H. (1981). Modern Development of Statistical Methods. In: Eykhoff, P. (Hrsg.) Trends and Progress in System Identification. Pergamon Press, Oxford, 169-184.

    Google Scholar 

  • Aldrich, J. (2000). Fisher’s ‘Inverse Probability’ of 1930. International Statistical Review 68(2), 155-172.

    MATH  Google Scholar 

  • Antoniou, G. (1997). Nonmonotonic Reasoning. MIT Press, Cambridge, Mass.

    MATH  Google Scholar 

  • Aristoteles (o. J.) Organon, S. 351. Zitiert nach: Digitale Bibliothek, Band 2: Philosophie, S. 3124. (Vgl. Aristioteles 1. Analytik, S. 142 der Übersetzung von J. H. von Kirchmann (1877).) Felix Meiner, Leipzig: Philosophische Bibliothek, Bd. 10.

    Google Scholar 

  • Baeyer, H.C. v. (2005). Information. The New Language of Science. Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Balasubramanian, V. (1997). Statistical Inference, Occam’s Razor, and Statistical Mechanics on the Space of Probability Distributions. Neural Computation 9, 349-368.

    MATH  Google Scholar 

  • Balasubramanian, V. (2005). MDL, Bayesian Inference, and the Geometry of the Space of Probability Distributions. Kapitel 3 in: Grünwald et al. (2005), 81-98.

    Google Scholar 

  • Barnard, G.A. (1968). Computers, Statistics, and Politics. In: Watts, D.G. (Hrsg.) The Future of Statistics. Academic Press, New York, 39-43.

    Google Scholar 

  • Barnard, G.A. (1971). Diskussionsbeitrag zu Villegas (1971) in Godambe and Sprott (1971), 414.

    Google Scholar 

  • Barnard, G.A. (1996b). Fragments of a Statistical Autobiography. Student 1, 257-268.

    Google Scholar 

  • Barnett, V. (1999). Comparative Statistical Inference. (3. Aufl.) Wiley, New York. 1. Aufl. 1973.

    MATH  Google Scholar 

  • Barron, A.; Rissanen, J.; and Yu, B. (1998). The Minimum Description Length Principle in Coding and Modeling. IEEE Transactions on Information Theory 44, 2743-2760.

    MathSciNet  MATH  Google Scholar 

  • Bartlett, M.S. (1990). Chance or Chaos? (Mit Diskussion) J. of the Royal Statistical Society, Ser. A 153(3), 321-347.

    MathSciNet  Google Scholar 

  • Basu, D. (1964). Recovery of Ancillary Information. Sankhya 21, 247-256. Zitiert nach Ghosh (1988), Chapter I, 1-19.

    Google Scholar 

  • Basu, D. (1969). Sufficiency and Invariance. In: Bose, R.C. et al. (Hrsg.) Essays in Probability and Statistics. University of North Carolina, Chapel Hill, NC, 61-84. Zitiert nach Ghosh (1988), Chapter VIII, 142/143-160.

    Google Scholar 

  • Basu, D. (1971). On the Logical Foundations of Survey Sampling. In: Ghosh (1988), Chapter XII, 201-233, und Chapter XIII “Discussions”, 234-244. Basiert auf “An Essay on the Logical Foundations of Survey Sampling, Part I” (mit Diskussion) in: Godambe and Sprott (1971), 203-242.

    Google Scholar 

  • Bennett, J.H. (1990). Statistical Inference and Analysis. Selected Correspondence of R.A. Fisher. Clarendon Press, Oxford.

    MATH  Google Scholar 

  • Beran, R. (2001). The Role Of Experimental Statistics. In: Saleh, A. K. Mohammed E. (Hrsg.) Data Analysis from Statistical Foundations. A Festschrift in Honour of the 75th Birthday of D.A.S. Fraser. Nova Science Publishers, New York, 257-274.

    Google Scholar 

  • Beran, R. (2008). Kommentar zu Davies (2008). J. of the Korean Statistical Society 37, 217-219.

    MathSciNet  Google Scholar 

  • Berger, A. (2001). Chaos and Chance. An Introduction to Stochastic Aspects of Dynamics. De Gruyter, Berlin.

    MATH  Google Scholar 

  • Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis. (2. Aufl.) Springer, New York: Springer Series in Statistics. 1. Aufl. 1980.

    MATH  Google Scholar 

  • Berger, J.O. (2000). Bayesian Analysis: A Look at Today and Thoughts on Tomorrow. Journal of the American Statistical Association 95, 1269-1276.

    MATH  Google Scholar 

  • Berger, V. (2005a). Selection Bias and Covariate Imbalances in Randomized Clinical Trials. Wiley, New York.

    Google Scholar 

  • Berger, J.O.; and Wolpert, R.L. (1988). The Likelihood Principle. (2. Aufl.) Institute of Mathematical Statistics, Hayward, CA: IMS Lecture Notes.

    Google Scholar 

  • Berger, V.; and Weinstein, S. (2004). Ensuring the Comparability of Comparision Groups: Is Randomization Enough? Controlled Clinical Trials 25, 515-524.

    Google Scholar 

  • Berkovitz, J.; Frigg, R.; and Kronz, F. (2006). The Ergodic Hierarchy, Randomness and Hamiltonian Chaos. Studies in History and Philsophy of Modern Physics 37, 661-691.

    MathSciNet  Google Scholar 

  • Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Springer, New York.

    MATH  Google Scholar 

  • Bjørnstad, J.F. (1996). On the Generalization of the Likelihood Function and the Likelihood Principle. J. of the American Statistical Association 91, 791-806.

    Google Scholar 

  • Bodendorf, F. (2006). Daten- und Wissensmanagement. (2. Aufl.) Springer, Berlin.

    Google Scholar 

  • Bortz, J. (2004). Statistik für Human- Und Sozialwissenschaftler. (6. Aufl.) Springer, Berlin.

    Google Scholar 

  • Box, G.E.P. (1962). Kommentar zu Birnbaum (1962). Journal of the American Statistical Association 57, 311-312.

    Google Scholar 

  • Box, G.E.P. (1976). Science and Statistics. Journal of the American Statistical Association 71, 791-799.

    MathSciNet  MATH  Google Scholar 

  • Box, G.E.P. (1990a). Kommentar zu Roberts (1990). Statistical Science 5, 390-391.

    Google Scholar 

  • Box, G.E.P. (1990b). Kommentar zu Shafer (1990b). Statistical Science 5, 448-449.

    Google Scholar 

  • Box, G.E.P.; Hunter, J.S.; and Hunter, W.G. (2005). Statistics for Experimenters. Design, Innovation, and Discovery. (2. Aufl.) Wiley, New York. 1. Aufl. 1978.

    MATH  Google Scholar 

  • Brillinger, D.R. (2002b). John Wilder Tukey (1915-2000). Notices of the AMS 49(2), 193- 201.

    MathSciNet  MATH  Google Scholar 

  • Brillinger, D.R.; Fernholz, L.T.; and Morgenthaler, S. (Hrsg., 1997). The Practice of Data Analysis. Essays in Honor of John W. Tukey. Princeton University Press.

    Google Scholar 

  • Broer, H.W. (2004). KAM theory: The legacy of Kolmogorov’s 1954 paper. Bull. Amer. Math. Soc. 41, 507-521.

    MathSciNet  MATH  Google Scholar 

  • Brooks, R. (1991). Intelligence Without Reason. Proceedings of 12th Int. Joint Conf. On Artificial Intelligence, Sydney, Australia, August 1991, 569-595.

    Google Scholar 

  • Breiman L. (2001). Statistical Modeling: The Two Culutures. Statistical Science 16(3), 199-231.

    MathSciNet  MATH  Google Scholar 

  • Buckland, S.T.; Burnham, K.P.; and Augustin, N.H. (1997). Model Selection: An Integral Part of Inference. Biometrics 53, 603-618.

    MATH  Google Scholar 

  • Buehler, R.J. (1971). Measuring Information and Uncertainty. (Mit Diskussion). In: Godambe and Sprott (1971), 330-341.

    Google Scholar 

  • Büning, H. (1991). Robuste und adaptive Tests. De Gruyter, Berlin.

    MATH  Google Scholar 

  • Calude, C.S. (2002). Information and Randomness. An Algorithmic Perspective. (2. Aufl.) Texts in Theoretical Computer Science: Springer, Berlin.

    MATH  Google Scholar 

  • Camilli, G. (1990). The Test of Homogeneity for 2×2 Contingency Tables: A Review of and Some Personal Opinions on the Controversy. Psychological Bulletin 108(1), 135-145.

    Google Scholar 

  • Cartwright, N. (2007). Hunting Causes and Using Them. Approaches in Philosophy and Econcomics. Cambridge University Press, Cambridge.

    Google Scholar 

  • Casella, G. (1992). Conditional Inference from Confidence Sets. In: Ghosh und Pathak (1992), 1-12.

    Google Scholar 

  • Chen, C.; Härdle, W.; and Unwin, A. (Hrsg., 2008). Handbook of Data Visualization. Springer, Berlin: Springer Handbooks of Computational Statistics.

    MATH  Google Scholar 

  • Chernoff, H. (1959). Sequential Design of Experiments. Ann. Math. Stat. 29, 755-770. Wiederabgedruckt in Kotz und Johnson (1993), 345-360.

    MathSciNet  Google Scholar 

  • Chernoff, H. (1986). Kommentar zu Efron (1986). The American Statistician 40(1), 5.

    MathSciNet  Google Scholar 

  • Church, A. (1940). On the Concept of a Random Sequence. Bulletin of the American Mathematical Society 46, 130-135.

    MathSciNet  Google Scholar 

  • Cochran, W.G. (1965). The Planning of Observational Studies of Human Populations. (Mit Diskussion) J. of the Royal Statistical Society, Ser. A 128, 134-155.

    Google Scholar 

  • Cornfield, J. (1966). Sequential Trials, Sequential Analysis and the Likelihood Principle. American Statistician 20(2), 18-23.

    MATH  Google Scholar 

  • Cox, D.R. (1958). Some Problems Connected with Statistical Inference. Annals of Mathematical Statistics 29, 357-372.

    MathSciNet  MATH  Google Scholar 

  • Cox, D.R. (1977). The Role of Significance Tests. Scand. J. of Statistics 4, 49-70.

    MATH  Google Scholar 

  • Cox, D.R. (1978). Foundations of Statistical Inference: The Case for Eclectism. (Mit Diskussion) Austr. J. of Statistics 20, 43-59.

    Google Scholar 

  • Cox, D.R. (1986). Some General Aspects of the Theory of Statistics. International Statistical Review 54(2), 117-126.

    Google Scholar 

  • Cox, D.R. (1995). The Relation between Theory and Application in Statistics. (Mit Diskussion) Test 4(2), 207-261.

    MathSciNet  MATH  Google Scholar 

  • Cox, D.R. (2006). Principles of Statistical Inference. Cambridge University Press, New York.

    MATH  Google Scholar 

  • Cox, G. (1957). Statistical Frontiers. J. of the American Statistical Organization 52, 1-10.

    Google Scholar 

  • Dalal, S.R.; Fowlkes, E.B.; and Hoadley, B. (1989). Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure. J. of the American Statistical Association 84, 945-957.

    Google Scholar 

  • Dantzig, D. van (1957). Statisitcal Priesthood (Savage on Personal Probabilities). Statistica Neerlandica 2, 1-16.

    Google Scholar 

  • Dawid, A.P. (1979). Conditional Independence in Statistical Theory. (Mit Diskussion) J.Royal Stat. Soc., Vol. B 41, 1-31.

    MathSciNet  MATH  Google Scholar 

  • Dawid, A.P. (1984). Present Position and Potential Developments: Some Personal Views, Statistical Theory, the Prequential Approach. (Mit Diskussion) J. Royal Stat. Soc., Vol. A 147, 278-292.

    MathSciNet  MATH  Google Scholar 

  • Dawid, A.P. (1990). Kommentar zu Bartlett (1990). J. of the Royal Statistical Society, Ser. A 153(3), 339-340.

    Google Scholar 

  • Dawid, A.P. (1991). Fisherian Inference in Likelihood and Prequential Frames of Reference. (Mit Diskussion) J. Royal Stat. Soc., Vol. B 53, 79-109.

    MathSciNet  MATH  Google Scholar 

  • Dawid, A.P. (1992). Prequential Analysis, Stochastic Complexity and Bayesian Inference. In: Bernardo, J.M.; Berger, J.; Dawid, A.P.; and Smith, A.F.M. (Hrsg.) Oxford University Press, Oxford: Bayesian Statistics 4, 109-125.

    Google Scholar 

  • Dawid, A.P. (2003). Causal Inference using Influence Diagrams: the Problem of Partial Compliance. Kapitel 2 in Green et al. (2003), 45-65.

    Google Scholar 

  • Deck, T. (2006). Der Itô-Kalkül: Einführung und Anwendungen. Springer, Berlin. DeCode genetics. Homepage: http://www.decode.com/

  • Diaconis, P. (1998). A Place for Philosophy? The Rise of Modeling in Statistical Science. Quarterly of Applied Mathematics 56(4), 797-806.

    Google Scholar 

  • Diaconis, P. (2006). Theories of Data Analysis: From Magical Thinking Through Classical Statistics. Kapitel 1 (S. 1-36) in: Hoaglin, D.C.; Mosteller, F.; and Tukey, J.W. (Hrsg., 2006). Exploring Data Tables, Trends, and Shapes. (2. Aufl.) Wiley, New York. 1. Aufl. 1985.

    Google Scholar 

  • Donoho, D.L.; Johnstone, I.M.; Jeffrey, C.H.; and Stern, A.S. (1992). Maximum Entropy and the Nearly Black Object. J. R. Stat. Soc., Ser. B 54(1), 41-81.

    MATH  Google Scholar 

  • Donoho, D.L. (2000). High-dimensional Data Analysis: the Curses and Blessings of Dimensionality. Preprint: Dept. of Statistics, Stanford University. Siehe auch www-stat.stanford.edu/donoho/lectures.html.

  • Doob, H.L. (1953). Stochastic Processes. Wiley, New York: Series in Probability and Mathematical Statistics.

    MATH  Google Scholar 

  • Dowe, D.L.; Gardner, S.; and Oppy, G. (2007). Bayes not Bust! Why Simplicity is No Problem for Bayesians. Brit. J. Phil. Sci. 58(4), 709-754.

    MathSciNet  MATH  Google Scholar 

  • Draper, D.; Hodges, J.S.; Mallows, C.L.; and Pregibon, D. (1993). Exchangeability and Data Analysis. J. Royal Stat. Soc. A 56(1), 9-37.

    MathSciNet  Google Scholar 

  • Durant, W. und Durant, A. (1985). Kulturgeschichte der Menschheit. (18 Bde.) Naumann & Göbel, Köln.

    Google Scholar 

  • Durbin, J. (1987). Statistics and Statistical Science. (Mit Diskussion) J. of the Royal Stat. Soc. A 150(3), 177-191.

    MathSciNet  Google Scholar 

  • Earman, J. (1992). Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. The MIT Press, Cambridge, Mass.

    Google Scholar 

  • Earman, J. (2004). Laws, Symmetry, and Symmetry breaking; Invariance, Conservation Principles, and Objectivity. Philosophy of Science 71, 1227-1241.

    MathSciNet  Google Scholar 

  • Edgington, E.S. (1995). Randomization Tests. (3. Aufl.) Reihe: Statistics, Textbooks and Monographs 147. Marcel Dekker, New York.

    Google Scholar 

  • Edwards, A.W.F. (1972). Likelihood. Johns Hopkins University Press, Baltimore, MD.

    MATH  Google Scholar 

  • Edwards, W.; Lindman, H.; and Savage, L.J. (1963). Bayesian Statistical Inference for Psychological Research. Psychological Review 70, 193-242. Wiederabgedruckt in Kotz und Johnson (1993: Bd. I, 531-578).

    Google Scholar 

  • Eerola, M. (1994). Probabilistic Causality in Longitudinal Studies. Springer, New York: Lecture Notes in Statistics, No. 92.

    MATH  Google Scholar 

  • Efron, B. (1978). Controversies in the Foundations of Statistics. American Math. Monthly 85(4), 232-246.

    MathSciNet  Google Scholar 

  • Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics 7, 1-26. Wiederabgedruckt in Kotz und Johnson (1993: Bd. II, 569-294).

    MathSciNet  MATH  Google Scholar 

  • Efron, B. (1986). Why isn’t Everyone a Bayesian? The American Statistician 40(1), 1-5.

    MathSciNet  MATH  Google Scholar 

  • Efron, B. (1990). Kommentar zu Shafer (1990b). Statistical Science 5(4), 450.

    Google Scholar 

  • Efron, B. (1993). Introduction to James und Stein (1961). In: Kotz und Johnson (1993: Bd. I, 437-442).

    Google Scholar 

  • Efron, B. (1998). R. A. Fisher in the 21st Century. (Mit Diskussion) Statistical Science 13(2), 95-122.

    MathSciNet  MATH  Google Scholar 

  • Efron, B. (2003). Robbins, Empirical Bayes and Microarrays. Annals of Statistics 31, 366- 378.

    MathSciNet  MATH  Google Scholar 

  • Einstein, A. (1952). Brief an M. Solovine vom 7. Mai. Einstein Archive, Database Record 21-283.00. Siehe auch von Baeyer (2005), 136-138.

    Google Scholar 

  • Ellis, S.P. (1993). Kommentar zu Draper et al. (1993). J. Royal Stat. Soc. A 56(1), 33.

    Google Scholar 

  • Evett, I.W. (2000). Kommentar zu Lindley (2000). The Statistician 49(3), 332-333.

    Google Scholar 

  • Falk, R. (1998). Replication - a Step in the Right Direction. Kommentar zu Sohn (1998).Theory & Psychology 8(3), 313-321.

    Google Scholar 

  • Feder, M. (1986). Maximum Entropy as a Special Case of the Minimum Description Length Criterion. IEEE Transactions on Information Theory 32(6), 847-849.

    MathSciNet  MATH  Google Scholar 

  • Feigl, H. (1970a). The “Orthodox” View of Theories: Remarks in Defense as well as Critique. In: Radner, M.; and Winokur, S. (Hrsg.) Minnesota Studies in the Philosophy of Science: Analyses of Theories and Methods of Physics and Psychology, Vol. IV, 3-16.

    Google Scholar 

  • Feynman, R.P. (2007). Vom Wesen physikalischer Gesetze (8. Aufl.) Piper.

    Google Scholar 

  • Finetti, B. de (2006). L’invenzione della verità. Cortina, Mailand. Zuvor unveröffentlichtes Manuskript aus dem Jahr 1934.

    Google Scholar 

  • Fischer, K. (1983). Rationale Heuristik. Die Funktion der Kritik im „Context of Discovery“. Zeitschrift für allgemeine Wissenschaftstheorie XIV(2), 234-272.

    Google Scholar 

  • Fisher, R.A. (1922). On the Mathematical Foundations of Theoratical Statistics. Philosophical Transactions of the Royal Society of London, Ser. A 222, 309-368. Zitiert nach Kotz und Johnson (1993: Bd. I, 11-44).

    Google Scholar 

  • Fisher, R.A. (1925). Theory of Statistical Estimation. Proceedings of the Cambridge Philos.Soc. 22, 200-225.

    Google Scholar 

  • Fisher, R.A. (1930). Inverse Probability. Proceedings of the Cambridge Philos. Soc. 26, 528-535.

    MATH  Google Scholar 

  • Fisher, R.A. (1936a). Tests of Significance Applied to Haldon’s Data on Partial Sex Linkage. Annaly of Eugenics 7, 87-104.

    Google Scholar 

  • Fisher, R.A. (1966). The Design of Experiments. (8. Aufl.) Hafner Publishing Company, New York. 1. Aufl. 1935, 4. Aufl. 1947.

    Google Scholar 

  • Fisher, R.A. (1970). Statistical Methods for Research Workers. (14. Aufl.) Macmillan, New York. 1. Aufl. 1925.

    Google Scholar 

  • Fisher, R.A. (1973). Statistical Methods and Scientific Inference. (3. Aufl.) Hafner Publishing Company, New York. 1. Aufl. 1956, 2. Aufl. 1959.

    MATH  Google Scholar 

  • Fraassen, B. van (1990). Laws and Symmetry. Oxford University Press, Oxford.

    Google Scholar 

  • Frankfurter Allgemeine Zeitung (2009). Artikelserie zur Volkswirtschaftslehre: Plickert, P. (20.1.) Gefangen in der Formelwelt; Hüther, M. (21.3.) Ordnungsökonomik fasziniert noch heute; Ritschl, A. (21.3.) Ordnungsökonomik war ein Sonderweg; Bachmann, R. und Uhlig, H. (29.3.) Die Welt ist nicht schwarz oder weiß; Nienhaus, L. und Siedenbiedel, C. (5.4.) Die Ökonomen in der Sinnkrise; Braunberger, G. (7.4.) In Krisen gehen auch Doktrinen unter; Vanberg, V. (13.4.) Die Ökonomik ist keine zweite Physik; 83 Professoren der Volkswirtschaftslehre (Aufruf vom 5.5.) Rettet die Wirtschaftspolitik an den Universitäten! ; Gehrig, T.P. (11.5.) Schadet es, wenn Ökonomen rechnen können? ; Plickert, P. (13.5.) Ökonomik in der Vertrauenskrise; Aus dem Moore, N. und Schmidt, C.M. (22.5.) Quo vadis, Ökonomik? ; Mussler, W. (16.6.) Die Lehren der Anderen; Sinn, H.-W. (22.6.) Der richtige Dreiklang der VWL; Nienhaus, L. (24.8.) Dreißig nutzlose Jahre; Interview mit Robert Shiller (30.8.) Die nächsten fünf Jahre werden enttäuschend. F.A.Z. Electronic Media GmbH, Frankfurt a. M.

    Google Scholar 

  • Fraser, D.A.S. (1961). The Fiducial Method and Invariance. Biometrika 48(3), 261-280.

    MathSciNet  MATH  Google Scholar 

  • Fraser, D.A.S. (1968). The Structure of Inference. Wiley, New York.

    MATH  Google Scholar 

  • Fraser, D.A.S. (1996). Some Remarks on Pivotal Models and the Fiducial Argument in Relation to Structural Models. International Statistical Review 64, 231-235.

    MATH  Google Scholar 

  • Fraser, D.A.S. (2004). Ancillaries and Conditional Inference. (Mit Diskussion) Statistical Science 19, 332-369.

    Google Scholar 

  • Freedman, D.A. (1997). From Association to Causation via Regression. Adv. Appl. Math. 18, 59-110.

    MATH  Google Scholar 

  • Freedman, D.A. (2005). Statistical Models: Theory and Practice. Cambridge University Press, New York.

    MATH  Google Scholar 

  • Freedman, D.A. (2006). Statistical Models for Causation. What Inferential Leverage do they Provide? Evaluation Review 30(6), 691-713.

    Google Scholar 

  • Freedman, D.A. (2008a). Randomization does not Justify Logistic Regression. Statistical Science 23 (2008), 237-249.

    MathSciNet  Google Scholar 

  • Freedman, D.A. (2010). Statistical Models and Causal Inference. A Dialogue with the Social Sciences. Posthum herausgegeben und mit einer Einleitung (S. i-xvi) versehen von Collier, D.; Sekhon, J.S.; and Stark, P.B. Cambridge University Press, New York.

    Google Scholar 

  • Friedman, J.H. (2001). The Role of Statistics in the Data Revolution? International Statistical Review 69(1), 5-10.

    Google Scholar 

  • Gács, P.; Tromp, J.T.; and Vitányi, P.M.B. (2001). Algorithmic Statistics. IEEE Transactions on Information Theory 47(6), 2443-2463.

    MATH  Google Scholar 

  • Gardner, M. (2001). A Skeptical Look at Karl Popper. Skeptical Inquirer, 25(4), 13-14, 72.

    Google Scholar 

  • Gelman, A; Carlin, J.B.; Stern, H.S.; and Rubin, D.B. (2004). Bayesian Data Analysis. CRC Press, Boca Raton, FL.

    MATH  Google Scholar 

  • Ghosh, M.; and Pathak, P.K. (Hrsg., 1992). Current Issues in Statistical Inference: Essays in Honor of D. Basu. Institute of Mathematical Statistics: Lecture Notes - Monograph Series.

    Google Scholar 

  • Gigerenzer, G.; Krauss, S. und Vitouch, O. (2004). The Null Ritual. What You Always Wanted to Know About Significance Testing but Were Afraid to Ask. Kapitel 21 in Kaplan (2004), 391-408.

    Google Scholar 

  • Gillies, D. (1993). Philosophy of Science in the Twentieth Century. Blackwell, Oxford.

    Google Scholar 

  • Gillies, D. (2000). Philosophical Theories of Probability. Routledge, London.

    Google Scholar 

  • Gillies, D. (2009). On Bruno de Finetti’s L’invenzione de la verità. In: Galavotti (2009), 249-257.

    Google Scholar 

  • Gilmour, S. G. (1995). Kommentar zu Chatfield (1995). J. of the Royal Statistical Society A 158(3), 450.

    MathSciNet  Google Scholar 

  • Glymour, C. (1998). What Went Wrong? Reflections on Science by Observation and the Bell Curve. Phil. of Science 65(1, 1-32.

    MathSciNet  Google Scholar 

  • Glymour, C.; Madigan, D.; Pregibon, D.; and Smyth, P. (1996). Statistical Inference and Data Mining. Communications of the ACM 39(11), 35-41.

    Google Scholar 

  • Glymour, C.; Scheines, R.; Spirtes, P.; and Kelly, K. (1987). Discovering Causal Structure. Artificial Intelligence, Philosophy of Science, and Statistical Modeling. Academic Press, San Diego.

    MATH  Google Scholar 

  • Good, I.J. (1950). Probability and the Weighing of Evidence. Charles Griffin, London.

    MATH  Google Scholar 

  • Good, I.J. (1971b). Kommentar zu Villegas (1971) in Godambe and Sprott (1971), 415.

    Google Scholar 

  • Good, I.J. (1976). The Bayesian Influence, or how to Sweep Subjectivism under the Carpet. In: Harper and Hooker (1976), 125-174.

    Google Scholar 

  • Good, I.J. (1983a). Good Thinking. The Foundations of Probability and Statistics. University of Minnesota Press, Mineapolis, MN.

    Google Scholar 

  • Good, I.J. (1988). The Interface between Statistics and Philosophy of Science. (Mit Diskussion) Statistical science 3(4), 386-412.

    MathSciNet  MATH  Google Scholar 

  • Gorski, P.S. (2004). The Poverty of Deductivism: A Constructive Realist Model of Sociological Explanation. Sociological Methodology 34, 1-34.

    Google Scholar 

  • Gosset, W. S.; Pseudonym “Student” (1908). The Probable Error of a Mean. Biometrika 6(1), 1-25. Wiederabgedruckt in Kotz und Johnson (1993: Bd. II, 33-58).

    Google Scholar 

  • Grace, J.B. (2006). Structural Equation Modeling and Natural Systems. Cambridge University Press, Cambridge.

    Google Scholar 

  • Greenland, S. (1990). Randomization, Statistics, and Causal Inference. Epidemiology 1(6), 421-429.

    Google Scholar 

  • Greenland, S.; Pearl, J.; and Robins, J.M. (1999). Causal Diagrams for Epidemiologic Research. Epidemiology 10(1), 37-48.

    Google Scholar 

  • Greeno, J.G. (1970). Evaluation of Statistical Hypotheses using Information Transmitted. Phil. of Science 37, 279-294.

    Google Scholar 

  • Grünwald, P.D. (2005). Introducing to the Minimum Description Length Principle. In: Grünwald et al. (2005), 3-21.

    Google Scholar 

  • Grünwald, P.D. (2007). The Minimum Description Length Principle. MIT Press.

    Google Scholar 

  • Guttman, L. (1985). The Illogic of Statistical Inference for Cumulative Science. Applied stochastic models and data analysis 1, 3-9.

    Google Scholar 

  • Hacking, I. (2001). An Introduction to Probabilty Theory and Inductive Logic. Cambridge University Press, Cambridge.

    Google Scholar 

  • Hahn, R.W.; and Tetlock, P.C. (Hrsg., 2006). Information Markets. A New Way of Making Decisions. The AEI Press, Washington D.C.

    Google Scholar 

  • Hájek, A. (2007). Interpretations of Probability, The Stanford Encyclopedia of Philosophy (Winter 2007 Edition), Edward N. Zalta (Hrsg.) Siehe http://plato.stanford.edu/archives/win2007/entries/probability-interpret

  • Hájek, A. (2008). Probability - A Philosophical Overview. In: Gould, B.; and Simons, R.A. (Hrsg.) Proof & and Other Dilemmas: Mathematics and Philosophy. The Mathematical Association of America, Washington D.C.: Spectrum Series.

    Google Scholar 

  • Hampel, F.R. (1996). On the Philosophical Foundations of Statistics: Bridges to Huber’s Work, and Recent Results. In: Rieder, H. (Hrsg.) Robust Statistics, Data Analysis, and Computer Intensive Methods. In Honor of Peter Huber’s 60th Birthday. Springer, Berlin, 185-196.

    Google Scholar 

  • Hampel, F.R. (2003). The Proper Fiducial Argument. Research Report 114. Seminar für Statistik der Eidgenössischen Technischen Hochschule (ETH) Zürich. Siehe http://ecollection.ethbib.ethz.ch/eserv/eth:26403/eth-26403-01.pdf

  • Hampel, F.R. (2005). The Proper Fiducial Argument. Electronic Notes in Discrete Mathematics 21, 297-300.

    Google Scholar 

  • Hand, D.J. (1998a). Breaking Misconceptions - Statistics and its Relationship to Mathematics. The Statistician 47(2), 245-250.

    Google Scholar 

  • Hand, D.J. (2007). Information Generation. How Data Rule our World. Oneworld Publications, Oxford.

    Google Scholar 

  • Härdle, W.; Müller, M.; Sperlich, S.; and Werwatz, A. (2004). Nonparametric and Semiparametric Models. Springer: Springer Series in Statistics.

    MATH  Google Scholar 

  • Harper, W.; and Wheeler, G. (Hrsg., 2007). Probability and Inference. Essays in Honor of Henry E. Kyburg, Jr. College Publications, London: Texts in Philosophy, Vol. 2.

    Google Scholar 

  • Hawthorne, J. (2005), Inductive Logic. In: Zalta, E.N. (Hrsg.) Stanford Encyclopedia of Philosophy.

    Google Scholar 

  • Heckman, J.J. (2005). The Scientific Model of Causality. (Mit einem Kommentar von Sobel, 99-133) Sociological Methodology 35, 1-162.

    Google Scholar 

  • Hennig, C. (2007). Falsification of Propensity Models by Statistical Tests and the Goodness-of-Fit Paradox. Philosophia Mathematica 15, 166-192.

    MathSciNet  MATH  Google Scholar 

  • Hill, A.B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, London 58(5), 295–300.

    Google Scholar 

  • Hill, J.R. (1990). A General Framework for Model-Based Statistics. Biometrika 77(1), 115-126.

    MathSciNet  MATH  Google Scholar 

  • Hjort, N.L.; Holmes, C.; Müller, P.; and Walker, S.G. (Hrsg., 2010). Bayesian Nonparametrics. Cambridge University Press, Cambridge: Cambridge Series in Statistical and Probabilistic Mathematics.

    MATH  Google Scholar 

  • Ho, D.E.; Imai, K.; King, G.; and Stuart, E.A. (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15, 199-236.

    Google Scholar 

  • Holland, P.W. (1986) Statistics and Causal Inference. (Mit Diskussion) J. of the American Statistical Association 81, 945-970.

    MathSciNet  MATH  Google Scholar 

  • Hotelling, H. (1940). The Teaching of Statistics. Ann. of Math. Statistis 11, 457-470. Wiederabgedruckt 1988 in: Statistical Science 3, 63-71.

    MathSciNet  Google Scholar 

  • Huber, P.J. (1997) Speculations on the Path of Statistics. In: Brillinger et al. (1997), 175- 191.

    Google Scholar 

  • Huber, P.J. (2006). Kommentar zu Mallows (2006). Technometrics 48(3), 332-334.

    MathSciNet  Google Scholar 

  • Hutter, M. (2007). On Universal Prediction and Bayesian Confirmation. Theoretical Computer Science, 384, 33-48.

    MathSciNet  MATH  Google Scholar 

  • James, W.; and Stein, C. (1961). Estimation with Quadratic Loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability; University of Berkeley Press, Berkeley, CA 1, 311-319. Wiederabgedruckt in Kotz und Johnson (1993: Bd. I, 443-460).

    Google Scholar 

  • Jaynes, E.T. (1957). Information Theory and Statistical Mechanics I, II. Physical Review, 106, 620-630 und 108, 171-190.

    MathSciNet  Google Scholar 

  • Jaynes, E.T. (1976). Confidence Intervals vs Bayesian Intervals. (Mit einem Kommentar von O. Kempthorne) in: Harper and Hooker (1976), 175-257.

    Google Scholar 

  • Jaynes, E.T. (2003). Probability Theory. The Logic of Science. Posthum herausgegeben von Bretthorst, G. L. Cambridge University Press, Cambridge.

    Google Scholar 

  • Jeffrey, R. (2004). Subjective Probability: the Real Thing. Cambridge University Press, Cambridge.

    MATH  Google Scholar 

  • Jeffreys, H. (1939). Theory of Probability. Clarendon Press, Oxford.

    Google Scholar 

  • Joe, H. (1989). Relative Entropy Measures of Multivariate Dependence. J. of the American Statistical Association 84, 157-164.

    MathSciNet  MATH  Google Scholar 

  • Johnstone, D.J. (1987a). Tests of Significance Following R. A. Fisher. Brit J. Phil. Sci. 38, 481-499.

    MathSciNet  Google Scholar 

  • Kaplan, D. (Hrsg., 2004). The SAGE Handbook of Quantitative Methodology for the Social Sciences. Sage Publications, Thousand Oaks, CA.

    Google Scholar 

  • Keiding, N. (1994). Kommentar zu Spiegelhalter et al. (1994). J. of the Royal Statistical Society, Ser. A 157 (3), 395.

    MathSciNet  Google Scholar 

  • Kemeny, J.G. (1953). The Use of Simplicity in Induction. The Philosophical Review 62, 391-408.

    Google Scholar 

  • Kempthorne, O. (1955). The Randomization Theory of Experimental Inference. J. of the American Stat. Assoc. 50, 946-967.

    MathSciNet  Google Scholar 

  • Kempthorne, O. (1979). The Design and Analysis of Experiments. Robert E. Krieger, New York.

    Google Scholar 

  • Khrennikov, A. (2009). Interpretations of Probability. (2. Aufl.) De Gruyter, New York.

    MATH  Google Scholar 

  • Kiefer, J. (1977). Conditional Confidence Statements and Confidence Estimators. (Mit Diskussion) J. of the American Statistical Association 72, 789-827.

    MathSciNet  MATH  Google Scholar 

  • Knight, F. (1921). Risk, Uncertainty, and Profit. Houghton Mifflin, New York.

    Google Scholar 

  • Köhler, P. (Hrsg., 2008). Die schönsten Zitate der Politiker. 1000 Weisheiten für Reden, E-Mails, Gästebuch, zum Vergnügen und zur Erkenntnis .Humboldt Verlags GmbH, Baden-Baden.

    Google Scholar 

  • Kolmogorov, A.N. (1933). Grundbegriffe der Wahrscheinlichkeitsrechnung. Springer, Berlin.

    Google Scholar 

  • Kolmogorov, A.N. (1954). The general theory of dynamical systems and classical mechanics. Proceedings of the International Congress of Mathematicians, Amsterdam, Niederlande 1, 315-333. North Holland, Amsterdam (1957). Englische Übersetzung in: Abraham, R.H. (1967). Foundations of Mechanics, Appendix D, 263-279. Benjamin, New York.

    Google Scholar 

  • Koopmans, T. (1949). Identification Problems in Economic Model Construction. Econometrica 46, 125-144.

    MathSciNet  Google Scholar 

  • Krämer, W. (2004). Statistik: Vom Geburtshelfer zum Bremser der Erkenntnis in den Sozialwissenschaften? Kölner Zeitschrift für Soziologie und Sozialpsychologie. Sonderheft 44/2004, 51-60.

    Google Scholar 

  • Krengel, U. (1988). Einführung in die Wahrscheinlichkeitstheorie und Statistik. vieweg, Braunschweig.

    MATH  Google Scholar 

  • Kruskal, W.H. (1988). Miracles and Statistics: The Casual Assumption of Independence. Journal of the American Statistical Association 83, 929-940.

    MathSciNet  Google Scholar 

  • Kullback, S. (1959). Information Theory and Statistics. Wiley, New York.

    MATH  Google Scholar 

  • Kullback, S. (1987). The Kullback-Leibler distance (Letter to the Editor). The American Statistician 41(4), 340-341.

    Google Scholar 

  • Ladyman, J. (2002). Understanding Philosophy of Science. Routledge, London.

    Google Scholar 

  • Lang, C. (2005). Theoretische und empirische Aspekte der Prognose wichtiger makroökonomischer Größen. Cuvillier Verlag, Göttingen.

    Google Scholar 

  • Lauritzen, S.L. (2001). Causal Inference from Graphical Models. Kapitel 2 (S. 63-107) in: Barndorff-Nielsen, O.E.; and Klüppelberg C. (Hrsg.) Complex Stochastic Systems. Chapman & Hall, London.

    Google Scholar 

  • Leamer, E.E. (1978). Specification Searches. Ad hoc Inference with Nonexperimental Data. Wiley, New York.

    MATH  Google Scholar 

  • Leamer, E.E. (1985). Sensitivity Analyses would Help. American Economic Review 75, 308-313.

    Google Scholar 

  • Lesaffre, E. (2000) Kommentar zu Senn (2000). The Statistician 49(2), 169.

    Google Scholar 

  • Lewis, D. (1973a) Causation. J. of Philosophy 70, 556-567.

    Google Scholar 

  • Lewis, D. (1973b) Counterfactuals. Harvard University Press, Cambridge, MA.

    Google Scholar 

  • Lindley, D.V. (1956). On a Measure of the Information Provided by an Experiment. The Annals of Mathematical Statistics 27, 986-1005.

    MathSciNet  MATH  Google Scholar 

  • Lindley, D.V. (1975). The Future of Statistics - A Bayesian 21st Century. Supp. Adv. Appl. Prob. 7, 106-115.

    Google Scholar 

  • Lindley, D.V. (1982a). Scoring Rules and the Inevitability of Probability. International Statistical Review 50, 1-26.

    MathSciNet  MATH  Google Scholar 

  • Lindley, D.V. (1995). Kommentar zu Cox (1995). Test 4(2), 242-245.

    Google Scholar 

  • Lindley, D.V. (1999b). Kommentar (S. 122-125) zu Bernardo, J.M. “Nested Hypothesis Testing: The BRC Criterion.” In: Bernardo et al. (1999), 101-130.

    Google Scholar 

  • Lindley, D.V. (2002). Letter to the Editor. Teaching Statistics 24(1), 22-23.

    MathSciNet  Google Scholar 

  • Lippe, P. von der (1996). Wirtschaftsstatistik. (5. Aufl.) UTB.

    Google Scholar 

  • Longford, N.T. (1999). Selection Bias and Treatment Heterogeneity in Clinical Trials. Statist. Med. 18, 1467-1474.

    Google Scholar 

  • Longford, N.T. (2000). Kommentar zu Senn (2000). The Statistician 49(2), 169-170.

    Google Scholar 

  • Lord, F.M. (1953). On the Statistical Treatment of Football Numbers. The American Psychologist 8, 750-751.

    Google Scholar 

  • Lorenz, E.N. (1963). Deterministic Nonperiodic Flow. Journal of the Atmospheric Sciences 20(2), 130-141.

    MathSciNet  Google Scholar 

  • Lorscheid, P. (2009). Statistik-Ausbildung im wirtschaftswissenschaftlichen Bachelor- Studium: Eine kommentierte Bestandsaufnahme an deutschen Universitäten. Wirtschafts- und Sozialstatistisches Archiv 3(4), 285-298.

    Google Scholar 

  • MacKay, D.J.C. (2005) Information Theory, Inference, and Learning Algorithms. (Version 7.2 vom 28.05.2005, 4th printing) Cambridge University Press, Cambridge.

    Google Scholar 

  • Mallows, C.L. (1973). Some Comments on Cp. Technometrics 15, 661-675.

    MATH  Google Scholar 

  • Mallows, C.L. (2006). Tukey’s Paper after 40 Years. (Mit Diskussion) Technometrics 48(3), 319-336.

    MathSciNet  Google Scholar 

  • Manski, C.F. (1999). Identification Problems in the Social Sciences. Harvard Univ. Press, Cambridge, MA.

    Google Scholar 

  • Manski, C.F. (2003). Partial Identification of Probability Distributions. Springer, Berlin.

    MATH  Google Scholar 

  • Manski, C.F. (2008). Identification for Prediction and Decision. Harvard Univ. Press, Cambridge, MA.

    Google Scholar 

  • Marden, J.I. (2000). Hypothesis Testing: From p Values to Bayes Factors 95, 1316-1320.

    Google Scholar 

  • Marshall, A.W.; Meza, J.C.; and Olkin, I. (2001). Can Data Recognize Its Parent Distribution? J. Comp. Grap. Stat. 10(3), 555-580.

    MathSciNet  Google Scholar 

  • Martin-Löf, P. (1966). The Definition of Random Sequences. Information and Control 9, 602-619.

    MathSciNet  Google Scholar 

  • Martin-Löf, P. (1974). The Notion of Redundancy and its use as a Qualitative Measure of the Discrepancy between a Statistical Hypothesis and a Set of Observational Data. Scand. Journal of Stat. 1, 3-18.

    MATH  Google Scholar 

  • Mayo, D.G. (1996). Error and the Growth of Experimental Knowledge. The University of Chicago Press, Chicago, IL.

    Google Scholar 

  • Meehl, P.E. (1978). Theoretical Risks and Tabular Asterisks: Sir Karl, Sir Ronald, and the Slow Progress of Soft Psychology. J. of Consulting and Clinical Psychology 46, 806-834.

    Google Scholar 

  • Menger, K. (1960). A Counterpart to Occam’s Razor in Pure and Applied Mathematics Ontological Uses. Synthese 12, 415-428.

    Google Scholar 

  • Miller, A.J. (1995). Kommentar zu Chatfield (1995). J. of the Royal Statistical Society A 158(3), 460.

    Google Scholar 

  • Minkler, J. (Hrsg., 2000). Logic-Based Artificial Intelligence. Springer.

    Google Scholar 

  • Mises, R. von (1919). Wahrscheinlichkeit, Statistik und Wahrheit. (4. Aufl. 1972) Springer, Wien.

    Google Scholar 

  • Morgan, A. de (1838). An Essay on Probabilites, and on Their Application to Life Contingencies and Insurance Offices. Longman, London.

    Google Scholar 

  • Nagel, E. (1979). The Structure of Science: Problems in the Logic of Scientific Explanation (2. Aufl.) Hackett Publishing Company, Indianapolis, IN.

    Google Scholar 

  • Nelder, J.A. (1999). Statistics for the Millenium. (Mit Diskussion.) The Statistician 48(2), 257-269.

    MathSciNet  Google Scholar 

  • Neumann, J. von (1947). The Mathematician. In: Heywood, R. B. (Hrsg.) The Works of the Mind. University of Chicago Press, Chicago, 180-196.

    Google Scholar 

  • Neyman, J. (1923). Sur les applications de la thar des probabilities aux experiences agaricales: Essay des principles. In Teilen 1990 wiederabgedruckt als On the Application of Probability Theory to Agricultural Experiments. Essay on Principles, Section 9, in: Statistical Science 5, 465-480.

    Google Scholar 

  • Neyman, J. (1934). On the Two Different Aspects or the Representative Method. J. of the Royal Statistical Society 47, 558-625.

    Google Scholar 

  • Neyman, J. (1957). Current Problems of Mathematical Statistics. In: Proc. Internat. Congress Mathematicians (Amsterdam, 1954) 1, 349–370. Noordhoff & North-Holland.

    Google Scholar 

  • Neyman, J. (1961). The Silver Jubilee of My Dispute with Fisher, Journal of the Operations Research Society of Japan, 3, 145-154.

    MathSciNet  Google Scholar 

  • Neyman, J. (1977). Frequentist Probability and Frequentist Statistics. Synthese 36, 97-131.

    MathSciNet  MATH  Google Scholar 

  • Nikulin, M.S. (2002). Neyman Structure. In: Hazewinkel, M. (Hrsg.) Encyclopedia of Mathematics. Springer. Siehe http://eom.springer.de/N/n066610.htm

  • Nozick, R. (2001). Invariances: The Structure of the Objective World. Belnap Press, Cambridge MA.

    Google Scholar 

  • O’Hagan, A. (1995). Kommentar zu Chatfield (1995). J. of the Royal Statistical Society A 158(3), 460.

    Google Scholar 

  • Oakes, M. (1986). Statistical Inference: A Commentary for the Social and Behavioral Sciences. Wiley, New York.

    Google Scholar 

  • Ornstein, D.S. (1989). Ergodic Theory, Randomness, and “Chaos”. Science 243, 182-187.

    MathSciNet  Google Scholar 

  • Parzen, E. (o. J.) Data Mining, Statistical Methods Mining and History of Statistics. Preprint: Department of Statistics, Texas A & M Univ. Siehe www.stat.tamu.edu/ftp/pub/eparzen/future.pdf und www.stat.tamu.edu/people/faculty/eparzen.html/

  • Pawitan, Y. (2001). In all Likelihood: Statistical Modelling and Inference Using Likelihood. Clarendon Press, Oxford.

    MATH  Google Scholar 

  • Pearl, J. (1995). Causal Diagrams for Empirical Research. (Mit Diskussion) Biometrika 82(4), 669-710.

    MathSciNet  MATH  Google Scholar 

  • Pearl, J. (2000a). Causality. Models, Reasoning and Inference. Cambridge University Press.

    Google Scholar 

  • Pearl, J. (2000b). Kommentar zu Dawid (2000). Journal of the American Statistical Association 95, 428-431.

    Google Scholar 

  • Pearl, J. (2009a). Causality. Models, Reasoning and Inference. (2. Aufl.) Cambridge University Press.

    Google Scholar 

  • Pearl, J. (2009b). Causal Inference in Statistics: An Overview. Statistics Surveys 3, 96-146.

    MathSciNet  MATH  Google Scholar 

  • Pearson, E.S. (1962). Some Thoughts on Statistical Inference. Ann. Math. Stat. 33(2), 394-403. Wiederabgedruckt in: The selected papers of E. S. Pearson (1966). Cambridge University Press, Cambridge.

    MATH  Google Scholar 

  • Penston, J. (2003). Fiction and Fantasy in Medical Research. The Large-Scale Randomised Trial. The London Press, London.

    Google Scholar 

  • Pitman, E.J.G. (1957). Statistics and Science. J. of the American Statistical Association 52, 322-330.

    MATH  Google Scholar 

  • Planck, M. (1913). Rektoratsrede vom 15.10.1913. In: Kretzschmar, H. (1967). Max Planck als Philosoph. E. Reinhardt Verlag, München, Basel.

    Google Scholar 

  • Popper, K.R. (1935). Logik der Forschung. Mohr Siebeck, Tübingen.

    Google Scholar 

  • Popper, K.R. (1959). The Propensity Interpretation of Probability, British Journal of the Philosophy of Science 10, 25-42.

    Google Scholar 

  • Popper, K.R. (1974). Objektive Erkenntnis. (2. Aufl.) Hoffmann und Campe, Hamburg.

    Google Scholar 

  • Post, H.R. (1971). Correspondence, Invariance and Heuristics: In Praise of Conservative Induction. Studies in history and philosophy of science 2(3), 213-255.

    Google Scholar 

  • Pukelsheim, F. (1993). Optimal Design of Experiments. Wiley, New York.

    MATH  Google Scholar 

  • Quenouille, M.H. (1949). Approximate Tests of Correlation in Time Series. J. of the Royal Statistical Society, Ser. B, 11, 18-44.

    MathSciNet  Google Scholar 

  • Rao, C.R. (1945). Information and Accuracy in the Estimation of Parameters. Bull. Calcutta Math. Soc. 37, 81-91. Wiederabgedruckt in Kotz und Johnson (1993: Bd. I, 235-248).

    MathSciNet  MATH  Google Scholar 

  • Rao, C.R. (2001). Linear Statistical Inference and its Applications. (2. Aufl.) Wiley Interscience. 1. Aufl. 1965.

    Google Scholar 

  • Reichenbach, H. (1949). The Theory of Probability. An Inquiry into the Logical and Mathematical Foundations of the Calculus of Probabilities. (2. Aufl.) University of California Press, Berkeley, CA.

    Google Scholar 

  • Reichenbach, H. (1956). The Direction of Time. University of California Press, Berkeley, CA.

    Google Scholar 

  • Reid, C. (1982). Neyman - From Life. Springer, New York.

    MATH  Google Scholar 

  • Reid, N. (1995). The Roles of Conditioning in Inference. Statistical Science 10(2), 138-199.

    MathSciNet  MATH  Google Scholar 

  • Rissanen, J. (1983). A Universal Prior for Integers and Estimation by Minimum Desription Length. Annals of Statistics 11(2), 416-431.

    MathSciNet  MATH  Google Scholar 

  • Rissanen, J. (1989). Stochastic Complexity in Statistical Inquiry. World Scientific, Singapore: Series in Computer Science 15.

    MATH  Google Scholar 

  • Rissanen, J. (2007). Information and Complexity in Statistical Modelling. Springer, New York.

    Google Scholar 

  • Robbins, H. (1956). An Empirical Bayes Approach to Statistics. In: Proceedings of the Third Berkeley Symposium on Mathematical Statistics 1, 157-163. University of California Press, Berkeley, CA. Wiederabgedruckt in Kotz und Johnson (1993: Bd. I, 388-394).

    Google Scholar 

  • Robbins, H. (1975). Wither Mathematical Statistics? Suppl. Adv. Appl. Prob. 7, 116-121.

    Google Scholar 

  • Robert, C.P. (2007). The Bayesian Choice. From Decision-Theoretic Foundations to Computational Implementation. (2. Aufl.) Springer, Berlin.

    MATH  Google Scholar 

  • Rodríguez, C. (2005) The ABC of model selection: AIC, BIC and the New CIC. In: Knuth, K. (Hrsg.) Bayesian Inference and Maximum Etnropy in Science and Engineering: 25th international Workshop at San José, California. August 7-12, 2005. AIP Conference Proceedings 803, 80-87.

    Google Scholar 

  • Rosenbaum, P.R. (1995). Kommentar zu Pearl (1995). Biometrika, 82(4), 698-699.

    Google Scholar 

  • Rosenbaum, P.R. (2002). Observational Studies. (2. Aufl.) Springer, New York: Springer Series in Statistics. 1. Aufl. 1995.

    MATH  Google Scholar 

  • Rosenthal, R. (1979). The ‘File Drawer Problem’ and Tolerance for Null Results. Psychological Bulletin 86, 638-641.

    Google Scholar 

  • Rothman, K.J. (Hrsg., 1988). Causal Inference. Epidemiology Resources Inc., Chestnut Hill, MA.

    Google Scholar 

  • Rothman, K.J.; Greenland, S.; and Lash, T.L. (2008). Modern Epidemiology. (3. Aufl.) Lippincott Williams & Wilkins.

    Google Scholar 

  • Rott, H. (1998). Making up One’s Mind: Foundations, Coherence, Nonmonotinicity. Oxford.

    Google Scholar 

  • Royall, R.M. (1997). Statistical Evidence. A Likelihood Paradigm. Chapman & Hall, London.

    MATH  Google Scholar 

  • Royall, R.M. (2000). On the Probability of Observing Misleading Statistical Evidence. (Mit Diskussion) J. of the American Statistical Association 95, 760-780.

    MathSciNet  MATH  Google Scholar 

  • Rubin, D.B. (1978). Bayesian Inference for Causal Effects: The Role of Randomization. Annals of Statistics 6, 34-58.

    MathSciNet  MATH  Google Scholar 

  • Rubin, D.B. (1990). Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies. Statistical Science 5(4), 472-480.

    MathSciNet  MATH  Google Scholar 

  • Rubin, D.B. (1991). Practical Implications of Modes of Statistical Inference for Causal Effects and the Critical Role of the Assignment Mechanism. Biometrics 4, 1213-1234. Zitiert nach Kapitel 24 in Rubin (2006), 402-425.

    Google Scholar 

  • Rubin, D.B. (1993). The Future of Statistics. Statistics and Computing 3, 204.

    Google Scholar 

  • Rubin, D.B. (2006). Matched Sampling for Causal Effects. Cambridge University Press, Cambridge.

    MATH  Google Scholar 

  • Ruspini, E. (1987). Epistemic Logics, Probability, and the Calculus of Evidence. Proceedings of the 10th International Joint Conference on Artifical Intelligence IJCAI) Elsevier, 924-931. Wiederabgedruckt als Kapitel 17 in Yager et al. (2008), 435-448.

    Google Scholar 

  • Russell, B. (1913). On the Notion of Cause. Proceedings of the Aristotelian Society (New Series) 13, 1-26.

    Google Scholar 

  • Rust, J. (1997). Using Randomization to Break the Curse of Dimensionality. Econometrica 65(3), 487-516.

    MathSciNet  MATH  Google Scholar 

  • Salmon, W.C. (1989). Four Decades of Scientific Explanation. University of Minnesota Press, Minnesota, MN.

    Google Scholar 

  • Salsburg, D.S. (1973). Sufficiency and theWaste of Information. The American Statistician 27(4), 152-154.

    Google Scholar 

  • Salsburg, D.S. (1985). The Religion of Statistics as practiced in Medical Journals. The American Statistician 39(3), 220-223.

    Google Scholar 

  • Sarstedt, M. (2006). Sample- and Segment-Size Specific Model Selection in Mixture Regession Analysis. A Monte Carlo Simulation Study. Discussion Paper No. 1252. Münchener Wirtschaftswissenschaftliche Beiträge (BWL). Siehe http://epub.ub.unimuenchen.de/1252/

  • Savage, L.J.H. (1954). The Foundations of Statistics. Wiley, New York.

    MATH  Google Scholar 

  • Savage, L.J.H. (1976). On Rereading R. A. Fisher. (Mit Diskussion) Annals of Statistics 4, 441-500.

    MathSciNet  MATH  Google Scholar 

  • Sawilowsky, S.S. (2002). Fermat, Schubert, Einstein, and Behrens–Fisher: The Probable Difference Between Two Means When \(\sigma _1^2 \, \ne \,\sigma _2^2 \). Journal of Modern Applied Statistical Methods, 1(2), 461-472.

    Google Scholar 

  • Schirach, F. von (2009). Verbrechen. (9. Aufl.) Springer, Wien.

    Google Scholar 

  • Schilling, R. (2005). Measures, Integrals and Martingales. Cambridge University Press, Cambridge.

    MATH  Google Scholar 

  • Schleichert, H. (1966). Elemente der physikalischen Semantik. Oldenbourg, Wien und München.

    Google Scholar 

  • Schurz, G. (2006). Einführung in die Wissenschaftstheorie. Wissenschaftliche Buchgesellschaft, Darmstadt.

    Google Scholar 

  • Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics 6(2), 461-464.

    MathSciNet  MATH  Google Scholar 

  • Sedlmeier, P. (1996). Jenseits des Signifikanztest-Rituals: Ergänzungen und Alternativen. Methods of Psychological Research Online 1(4), 41-63.

    Google Scholar 

  • Selvin, H; and Stuart, A. (1966). Data Dredging procedures in Survey Analysis. The American Statistician 20(3), 20-23.

    Google Scholar 

  • Senn, S. (2000). Consensus and Controversy in Pharmaceutical Statistics. (Mit Diskussion) The Statistician 49(2), 135-176.

    Google Scholar 

  • Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ.

    MATH  Google Scholar 

  • Shafer, G. (1990a). Perspectives on the Theory and Practice of Belief Functions. J. of Approximate Reasoning 4, 323-362.

    MathSciNet  MATH  Google Scholar 

  • Shafer, G. (1990b). The Unity and Diversity of Probability. (Mit Diskussion) Statistical Science 5(4), 435-462.

    MathSciNet  MATH  Google Scholar 

  • Shafer, G. (1996). The Art of Causal Conjecture. The MIT Press, Cambridge, MA.

    MATH  Google Scholar 

  • Shahar, E. (1997). A Popperian Perspective of the Term ‘Evidence-Based’ Medicine. Journal of Evaluation in Clinical Practice 3(2), 109-116.

    Google Scholar 

  • Shannon, C.E. (1948). A Mathematical Theory of Communication. The Bell System Technical Journal 27, 379-423 und 623-656.

    MathSciNet  MATH  Google Scholar 

  • Shipley, B. (2000). Cause and Correlation in Biology. A User’s Guide to Path Analysis, Structural Equations and Causal Inference. Cambridge University Press, Cambridge.

    Google Scholar 

  • Sinkkonen, J. (2002). What is the Curse of Dimensionality? Teil 2 von 7 des Dokuments comp.ai.neural-nets FAQ. (comp.ai.neural-nets ist eine Usenet Newsgroup.) Siehe www.faqs.org/faqs/ai-faq/neural-nets/part2/section-13.html

  • Smith, R. (2003). Medical Journals and Pharmaceutical Companies: Uneasy Bedfellows. British Medical Journal 326, 1202–1205.

    Google Scholar 

  • Sobel, M.E. (1995). Causal Inference in the Social and Behavioral Sciences. In: Arminger, G.; Clogg, C.C.; and Sobel, M.E. (Hrsg.) Handbook of Statistical Modeling for the Social and Behavioral Sciences. Plenum, New York, 1-38.

    Google Scholar 

  • Sobel, M.E. (2005). Kommentar zu Heckman (2005). Sociological Methodology 35, 99-133.

    Google Scholar 

  • Sober, E. (2004). The Contest between Parsimony and Likelihood. Syst. Biol. 53(4), 644-653.

    Google Scholar 

  • Sokal, A.D.; and Bricmont, J. (1998). Fashionable Nonsense. Postmodern Intellectuals’ Abuse of Science. Picador, New York.

    Google Scholar 

  • Solomonoff, R. (1964). A Formal Theory of Inductive Inference, Parts I and II. Information and Control 7, 1-22, 224-254.

    MathSciNet  MATH  Google Scholar 

  • Soofi, E.S. (1994). Capturing the Intangible Concept of Information. J. of the American Statistical Association 89, 1243-1254.

    MathSciNet  MATH  Google Scholar 

  • Soofi, E.S. (2000). Principal Information Theoretic Approaches. J. of the American Statistical Association 95, 1349-1353.

    MathSciNet  MATH  Google Scholar 

  • Speed, T. (2006). Terence’s Stuff: Bayes Forever. IMS Bulletin, 7.

    Google Scholar 

  • Spiegelhalter, D.J.; Best, N.G.; Carlin, B.P.; and van der Linde, A. (2002). Bayesian Measures of Complexity and Fit. (Mit Diskussion) J. of the Royal Statistical Society, Ser. B 64(4), 583-639.

    MATH  Google Scholar 

  • Spirtes, P.; Glymour, C.; and Scheines, R. (2000). Causation, Prediction, and Search. (2. Aufl.) The MIT Press, Cambridge, MA.

    Google Scholar 

  • Spohn, W. (1988). Ordinal Conditional Functions. A Dynamic Theory of Epistemic States. In: Harper, W.L.; and Skyrms, B. (Hrsg.) Causation in Decision, Belief Change, and Statistics, Vol. II. Springer, Berlin, 105-134.

    Google Scholar 

  • Spohn, W. (1990). Direct and Indirect Causes. Topoi 9, 125-145.

    MathSciNet  Google Scholar 

  • Sprent, P. (1998). Satistics and Mathematics - Trouble at the Interface? The Statistician 47(2), 239-244.

    Google Scholar 

  • Steiger, J.H. (1990). StructuralModel Evaluation and Modification: An Interval Estimation Approach. Multivariate Behavioral Research 25, 173-180.

    Google Scholar 

  • Stein, C. (1956). Inadmissibility of the Usual Estimator for the Mean of a Multivariate Normal Distribution. Proceedings of the Third Berkeley Smposium on Mathematical Statistics and Probability; University of Berkeley Press, Berkeley, CA 1, 197-206.

    Google Scholar 

  • Stigler, S.M. (1986). The History of Statistics. The Measurement of Uncertainty before 1900. The Belknap Press of Harvard University Press, Cambridge, MA.

    MATH  Google Scholar 

  • Stone, M. (1977). An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion. J. of the Royal Statistical Society, Ser. B 39, 44-47.

    MATH  Google Scholar 

  • Stone, R. (1993). The Assumptions on which Causal Inferences rest. J. of the Royal Statistical Society, Ser. B 55(2), 455-466.

    MATH  Google Scholar 

  • Stove, D. (2000). Scientific Irrationalism. Origins of a Postmodern Cult. Transaction Publishers, New Brunswick. Zunächst publiziert unter dem Titel “Anything Goes: Origins of the Cult of Scientific Irrationalism”, Macleay Press, 1998.

    Google Scholar 

  • Strevens, M. (1998). Inferring Probabilities from Symmetries. Noûs 32(2), 231-246.

    MathSciNet  Google Scholar 

  • Studený, M. (2005). Probabilistic Conditional Independence Structures. Springer, New York: Information Science and Statistics.

    MATH  Google Scholar 

  • Sunstein, C.R. (2009). Infotopia. Suhrkamp, Frankfurt a.M.

    Google Scholar 

  • Suppes, P. (1982). Arguments for Randomizing. In: Asquith,P.D.; and Nickles, T. (Hrsg.) PSA 1982. Proceedings of the 1982 Biennial Meeting of the Philosophy of Science Association, Bd. 2 „Symposia“. Philosophy of Science Association, East Lansing, MI, 464-475.

    Google Scholar 

  • Suppes, P. (1988). Kommentar zu Good (1988). Statistical Science 3(4), 398-400.

    Google Scholar 

  • Suppes, P. (2001). Representation and Invariance of Scientific Structures: Problems of Representation and Invariance (CSLI Lecture Notes). Center for Study of Language & Information.

    Google Scholar 

  • Suppes, P. (2009). Some Philosophical Reflections on de Finetti’s Thought. In: Galavotti (2009), 19-39.

    Google Scholar 

  • Tarski, A. (1986). Der Wahrheitsbegriff in den formalisierten Sprachen. In: Berka, K.; und Kreiser, L. (Hrsg.) Logik-Texte. Kommentierte Auswahl zur Geschichte der modernen Logik. (4. Aufl.) Akademie-Verlag, Berlin.

    Google Scholar 

  • Topsøe, F. (2007). Information Theory at the Service of Science. In: Csiszár, I; Katona, G. O. H.; and Tardos, G. (Hrsg.) Entropy, Search, Complexity. Springer: János Bolyai Mathematical Society 16, 179-208.

    Google Scholar 

  • Tukey, J.W. (1951). Standard Methods of Analyzing Data. Proceedings Compuatation Seminar der International Business Machines Corporation (IBM) in Armonk, New York, 706-731. Zitiert nach Kapitel 2 (S. 15-63) in Jones (1986a).

    Google Scholar 

  • Tukey, J.W. (1954). Unsolved Problems of Experimental Statistics. J. of the American Statistical Association 49, 706-731. Zitiert nach Kapitel 4 (S. 77-105) in Jones (1986a).

    Google Scholar 

  • Tukey, J.W. (1958). Bias and Confidence in not-quite large Samples. Annals of Mathematical Statistics 29, 614.

    Google Scholar 

  • Tukey, J.W. (1960a).Where do we go from here? J. of the American Statistical Association 52, 80-91. Zitiert nach Kapitel 5 (S. 107-126) in Jones (1986a).

    MathSciNet  Google Scholar 

  • Tukey, J.W. (1961). Statistical and Quantitative Methodology. In: Trends in Social Science. Ray, D.P. (Hrsg.) Philosophical Library, Inc., New York, 84-136. Zitiert nach Kapitel 7 (S. 143-181) in Jones (1986a).

    Google Scholar 

  • Tukey, J.W. (1962). The Future of Data Analysis. Annals of Mathematical Statistics 33, 1-67. Zitiert nach Kapitel 9 (S. 391-484) in Jones (1986a). Wiederabgedruckt in Kotz und Johnson (1993: Bd. II, 408-452).

    MathSciNet  MATH  Google Scholar 

  • Tukey, J.W. (1969). Analyzing Data: Sanctification or Detective Work? American Psychologist 24, 83-91. Zitiert nach Kapitel 16 (S. 721-740) in Jones (1986b).

    Google Scholar 

  • Tukey, J.W. (1973a). Comment. Proceedings of the First Canadian Conference in Applied Statistics “Statistics ’71 Canada”, 96-104. Zitiert nach Kapitel 19 (S. 777-791) in Jones (1986b).

    Google Scholar 

  • Tukey, J.W. (1973b). Exploratory Data Analysis as Part of a Larger Whole. Proceedings of the Eighteenth Conference on the Design of Experiments in Army Research Development and Testing, Part 1. The Army Mathematics Steering Committee. Zitiert nach Kapitel 20 (S. 793-804) in Jones (1986b).

    Google Scholar 

  • Tukey, J.W. (1977). Exploratory Data Analysis. Addison-Wesley: Behavioral Science; Quantitative Methods.

    Google Scholar 

  • Tukey, J.W. (1982). Discussion. Auszug aus The Role of Statistical Graduate Training in: Rustagi, J.S.; and Wolfe, D.A. (Hrsg.) Teaching of Statistics and Statistical Consulting. Academic Press, New York, 379-389. Zitiert nach Kapitel 26 (S. 881-889) in Jones (1986b).

    Google Scholar 

  • Tukey, J.W. (1984). Data Analysis: History and Prospects. In: David, H. A.; and David, H.T. (Hrsg.) Statistics: An Appraisal. Iowa State University Press, Ames, IA. Zitiert nach Kapitel 29 (S. 985-1001) in Jones (1986b).

    Google Scholar 

  • Tukey, J.W. (1986a). Foreword to the Philosophy Volumes. In: Jones (1986a) und Jones (1986b), xxxix-xliv.

    Google Scholar 

  • Tukey, J.W. (1986b). Data Analysis and Behavioral Science or Learning to Bear the Quantitative Man’s Burden by Shunning Badmandments. Kapitel 8 (S. 187-390) in Jones (1986a). Zuvor unveröffentlichtes Manuskript aus dem Jahr 1961.

    Google Scholar 

  • Tukey, J.W. (1986e). Do Derivations come from Heaven? Kapitel 25 (S. 875-880) in Jones (1986b). Zuvor unveröffentlichtes Manuskript aus dem Jahr 1981.

    Google Scholar 

  • Tukey, J.W. (1986 g). Diskussionsbeiträge zu Heckman und Robb (1986) in Wainer (1986), 58-62 und 108-110.

    Google Scholar 

  • Tukey, J.W. (1988). Lags in Statistical Technology. In: Carter, C.S.; Dwividi, T.D.; Fellegi, I.P.; Fraser, D.A.S.; McGregor, J.P.; and Sprott, D.A. (Hrsg.) Proceedings of the First Canadian Conference in Applied Statistics, 96-104.

    Google Scholar 

  • Tukey, J.W. (1997). More Honest Foundations for Data Analysis. J. of Statistical Planning and Inference 57, 21-28.

    MATH  Google Scholar 

  • Upshur, R.E.G. (2001). The Ethics of Alpha: Reflections on Statistics, Evidence and Values in Medicine. Theoretical Medicine 22, 565-576.

    Google Scholar 

  • Vella, F. (1998). Estimating Models with Sample Selection Bias: A Survey. The Journal of Human Ressources 33(1), 127-169.

    Google Scholar 

  • Venn, J. (1888). The Logic of Chance. (3. Aufl.) Macmillan, London. 1. Aufl. 1866.

    Google Scholar 

  • Vitányi, P. (2007). Algorithmic Chaos and the Incompressibility Method. Kapitel 15 in Charpentier et al. (2007), 301-317.

    Google Scholar 

  • Vovk, V. (2001). Competitive On-line Statistics. Int. Stat. Review 69(2), 213-248.

    MATH  Google Scholar 

  • Wald, A. (1947). Sequential Analysis. Wiley, New York.

    MATH  Google Scholar 

  • Wallace, C.S. (2005). Statistical and Inductive Inference by Minimum Message Length. Springer, New York. Serie: Information Science and Statistics.

    MATH  Google Scholar 

  • Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London.

    MATH  Google Scholar 

  • Wang, C. (1993). Sense and Nonsense of Statistical Inference. Controversy, Misuse and Subtlety. Marcel Dekker, New York.

    MATH  Google Scholar 

  • Weakliem, D.L. (1999). A Critique of the Bayesian Information Criterion for Model Selection. Sociological Methods & Research 27(3), 359-397.

    Google Scholar 

  • Weed, D.L. (1986). On the Logic of Causal Inference. American J. of Epidemiology 123(6), 965-979.

    Google Scholar 

  • Wegman, E.J. (1988). On Randomness, Determinism and Computability. J. of Statistical Planning and Inference 20, 279-294.

    MathSciNet  MATH  Google Scholar 

  • Weichselberger, K. (2001). Elementare Grundbegriffe einer allgemeineren Wahrscheinlichkeitsrechnung I. Intervallwahrscheinlichkeit als umfassendes Konzept. Physica-Verlag, Heidelberg.

    MATH  Google Scholar 

  • Weinberg, S. (1992). Dreams of a Final Theory. Pantheon Books, New York.

    Google Scholar 

  • Werndl, C. (2009). What are the New Implications of Chaos for Unpredictability? Brit. J. Phil. Sci. 60, 195-220.

    MathSciNet  MATH  Google Scholar 

  • Weyl, H. (1983). Symmetry. Princeton University Press, Princeton, NJ. 1. Aufl. 1952.

    Google Scholar 

  • Wigner, E. (1949). Invariance in Physical Theory. Proceedings of the American Philosophical Society 93(7), 521-526.

    Google Scholar 

  • Wikipedia (2009). Stichwort “Imprecise Probability”, Version vom 27. 3. 2009. Siehe http://en.wikipedia.org/wiki/Imprecise_probability

  • Williamson, J. (2007). Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities. In: Harper und Wheeler (2007), 151-179.

    Google Scholar 

  • Wilson, E.O. (2000). Die Einheit des Wissens. Goldmann, München.

    Google Scholar 

  • Woodward, J. (2003). Scientific Explanation. In: Edward N. Zalta (Hrsg.) The Stanford Encyclopedia of Philosophy (Summer 2003 Edition). Siehe http://plato.stanford.edu/archives/sum2003/entries/scientific-explanation/

  • Worrall, J. (2007). Why There’s No Cause to Randomize. Brit. J. Phil. Sci. 58, 451-488.

    MATH  Google Scholar 

  • Wright, S. (1921). Correlation and Causation. J. of Agricultural Research 20, 557-585.

    Google Scholar 

  • Yager, R. (1983). Entropy and Specificity in ‘A Mathematical Theory of Evidence.’ International Journal of General Systems 9(4), 249-260. Wiederabgedruckt als Kapitel 11 in Yager et al. (2008), 291-310.

    MathSciNet  MATH  Google Scholar 

  • Yager, R.R.; Liu, L. (Hrsg.); Dempster, A.P.; and Shafer, G. (beratende Hrsg., 2008). Classic Works of the Dempster-Shafer Theory of Belief Functions. Springer, Berlin.

    MATH  Google Scholar 

  • Yates, F. (1984). Tests of Significance for 2×2 Contingeny Tables. J. of the Royal Statistical Society, Ser. A 147(3), 426-463.

    MathSciNet  MATH  Google Scholar 

  • Ye, J. (1998). On Measuring and Correcting the Effects of Data Mining and Model Selection. J. of the American Statistical Association 93, 120-131.

    MATH  Google Scholar 

  • Zurek, W.H. (1989). Alogrithmic Randomness and Physical Entropy. Physical Review A 40(8), 4731-4751.

    MathSciNet  Google Scholar 

  • Zweig, S. (1977). Joseph Fouché. Bildnis eines politischen Menschen. Fischer, Frankfurt a. M.

    Google Scholar 

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Saint-Mont, U. (2011). Synthese. In: Statistik im Forschungsprozess. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2723-1_5

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