Abstract
Sketches are given of six applications of simulation in research on linear models. Fairly full references are given to more extended treatments of the topics, as they are to recent developments in the generation of pseudorandom numbers and variables.
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References
Andrews, D. F. A note on the selection of data transformations. Biometrika 58 (1971), 249–254.
Andrews, D. F., Bickel, P., Hampel, F., Huber, P., Rogers, W. H. And Tukey, J. “Robust Estimates of Location”. Princeton, N. J.: Princeton (1972).
Akaike, H. Information theory and an extension of the maximumlikelihood principle. Pp. 267–281 of “Proceedings of the Second International Symposium on Information Theory” (ed B. N. Petrov and F. Csaki). Budapest: Akademie Kiado. (1973).
Atkinson, A. C. A method for discriminating between models (with discussion). J. R. Statist. Soc. B 32 (1970), 323–353.
Atkinson, A. C. Testing transformations to normality. J. R. Statist. Soc. B 35 (1973), 473–479.
Atkinson, A. C. A note on the generalized information criterion for choice of a model. Biometrika 67 (1980a), 413–418.
Atkinson, A. C. Tests of pseudo-random numbers. Appl. Statist. 29 (1980b), 164–171.
Atkinson, A. C. Two graphical displays for outlying and influential observations in regression. Biometrika 68 (1981), 13–20.
Atkinson, A. C. Regression diagnostics, transformations and constructed variables (with discussion). J. R. Statist. Soc. B 44 (1982), 1–36.
Atkinson, A. C. “Plots, Transformations and Regression”. Oxford: Oxford University Press. (1985).
Belsley, D. A., Kuh, E. And Welsch, R. E. “Regression Diagnostics: Identifying Influential Data and Souuces of Collinearity.” New York: Wiley.
Bock, J. Robuste Regression. Pp. 123–142 of “Probleme der angewandten Statistik Heft 7: Robustheit III (Autorenkollektiv unter Leitung von D. Rasch und G. Herrendörfer).” Rostock: For-schungszentrum für Tierproduktion Dummerstorf-Ro-stock.
Box, G. E. P. Sampling and Bayes inference in scientific model building and robustness (with discussion). J. R. Statist. Soc. A 143 (1980), 383–430.
Box, G. E. P. and Cox, D. R. An analysis of transformations (with discussion). J. R. Statist. Soc. B 26 (1964), 211–246.
Cook, R. D. Detection of influential observations in linear regression. Technometrics 19 (1977), 15–18.
Cook, R. D. and Weisberg, S. “Residuals and Influence in Regression”. New York and London: Chapman and Hall (1982).
Cook, R. D. Comment on Huber. J. Amer. Statist. Assoc. 78 (1983), 74–75.
Cox, D. R. Tests of separate families of hypotheses. Proc. 4th Berkeley Symposium 1 (1961), 105–123.
Cox, D. R. Further results on tests of separate families of hypotheses. J. R. Statist. Soc. B 24 (1962), 406–424.
Cox, D. R. and Mccullagh, P. Some aspects of analysis of covariance. Biometrics 38 (1982), 541–561.
Hannan, E. J. and Quinn, B. G. The determination of the order of an autoregression. J. R. Statist. Soc. B 41 (1979), 190–195.
Hoaglin, D. C. and Welsch, R. E. The hat matrix in regression and ANOVA. Amer. Statist. 32 (1978) 17–22.
Huber. P. “Robust Statistics”.New York: Wiley (1981).
Huber, P. Minimax aspects of bounded-influence regression. J. Amer. Statist. Assoc. 78 (1983) 66–72.
Kinderman, A. J. and Monahan, J. F. Computer generation of random variables using the ratio of uniform deviates. A. C. M. Trans. Math. Soft. 3 (1977), 257–260.
Knuth, D. E. “The Art of Computer Programming. Volume 2 Seminumerical Algorithms”. Second Edition. Reading, Mass: Addison-Wesley.
Krasker, W. S. and Welsch, R. E. Efficient bounded influence regression estimation. J. Amer. Statist. Assoc. 77 (1982), 595–604.
Mallows, C. L. Some comments on Cp. Technometrics 15 (1973), 661–675.
Marriott, F. H. C. Barnard’s Monte-Carlo test: how many simulations? Appl. Statist. 28 (1979), 75–77.
McCullagh, P. and Nelder, J. A. “Generalized Linear Models”. London: Chapman and Hall.
Nelder, J. A. and Wedderburn, R. Generalized linear models. J. R. Statist. Soc. A 135 (1972) 370–384.
Ripley, B. D. Multidimensional randomness. Pp 241–247 of “Probability, Statistics and Analysis”(ed. J. F. C. Kingman and G. E. H. Reuter). Cambridge: Cambridge University Press. (1983a).
Ripley, B. D. Computer generation of random variables — a tutorial. Int. Statist. Rev. 51 (1983b) 301–319.
Tukey, J. W. One degree of freedom for nonadditivity. Biometrics 5 (1949) 232–242.
Williams, D. A. GLIM and Hirayama’s data. RSS News and Notes, 9(4) (1982) 7.
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© 1984 Academy of Agricultural Sciences of the GDR, Research Centre of Animal Production, Dummerstorf-Rostock, DDR 2551 Dummerstorf.
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Atkinson, A.C. (1984). Simulation in Research on Linear Models. In: Rasch, D., Tiku, M.L. (eds) Robustness of Statistical Methods and Nonparametric Statistics. Theory and Decision Library, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-6528-7_2
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DOI: https://doi.org/10.1007/978-94-009-6528-7_2
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