An Overview of “SCMA II”

  • P. J. Bickel
Conference paper


The goals of this conference (and those of “SCMA I”) [FB1992] were three fold.
  1. i)

    To expose statisticians to the statistical challenges posed by the explosive growth in the amount and complexity of astronomical data and in astrophysical theory and experimentation during the second half of this century.

  2. ii)

    To expose astronomers to developments in statistical methodology which might be helpful in the analysis of astronomical data

  3. iii)

    To introduce the two communities to each other with a view to the establishment of fruitful collaborations.



Training Sample Light Curf Astronomical Data Gravitational Lensing Effect High Order Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [A 89]
    Aldous, D. Probability Approximations via the Poisson Clumping Heuristic. Springer Verlag (1989).CrossRefMATHGoogle Scholar
  2. [BB 96]
    Bickel, P. J. and Buhlmann, P. What is a linear process? To appear in Proceedings National Academy of Sciences USA (1996).Google Scholar
  3. [BFOS 84]
    Breiman, L., Friedman, J., Olshen, R. and Stone, C. Classifica- tion and Regression Trees. Wadsworth (1984).Google Scholar
  4. [J 61]
    Jeffreys H. Theory of Probability. 3rd Edition. Oxford U. Press (1961).Google Scholar
  5. [FB 92]
    Feigelson E. D. and Babu, G. J. Statistical Challenges in Mod-ern Astronomy. Springer Verlag (1992).CrossRefGoogle Scholar
  6. [G 52]
    Gamow, G. The Birth and Death of the Sun. Viking Press (1952).Google Scholar
  7. [DLR 77]
    Dempster, A., Laird, N., and Rubin, D. Maximum likelihood es-timation for complete data via the EM algorithm. JRSS B 39, 1–38.Google Scholar
  8. [BEPSW 70]
    Brown, L., Eagon, J., Petrie, T., Soules, G., and Weiss, N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov Chain. Ann. Math. Statist. 41, 164–171.Google Scholar
  9. [D 92]
    Daubechies, J. Ten Lectures on Wavelets.Society for Industrial and Applied Mathematics (1992).CrossRefMATHGoogle Scholar
  10. [G 81]
    Grenander, U. Abstract Inference. J. Wiley (1981).MATHGoogle Scholar
  11. [B 79]
    Box, G. E. P. Some problems of statistics and every day life. J. Amer. Statist. Assoc. 74, 1–4 (1979).CrossRefGoogle Scholar
  12. [CH 92]
    Chambers, J. M. and Hastie, T. J. Statistical Models in S. Wadsworth/Brooks Cole (1992).MATHGoogle Scholar
  13. [LZ 86]
    Linhart, H. and Zucchini, W. Model Selection. J. Wiley (1986).MATHGoogle Scholar
  14. [B 95]
    Breiman, L. Bagging predictors. Machine Learning (in press) (1946).Google Scholar
  15. [L 86]
    Lehmann, E. L. Testing Statistical Hypotheses. Second Edi-tion. J. Wiley/Chapman Hall/Springer Verlag.Google Scholar
  16. [MT 77]
    Mosteller, F. and Tukey, J. W. Data Analysis and Regression. Adderson Wesley (1977).Google Scholar
  17. [BRG 90]
    Brown, L. D. and Gajek, L. Information inequalities for the Bayes risk. Ann. Stat. 18, 1578–1594 (1990).MathSciNetCrossRefMATHGoogle Scholar
  18. [GL 95]
    Gill, R. D. and Levit, B. Y. Applications of the van Trees inequal-ity: a Bayesian Cramer-Rao bound. Bernoulli 1, 059–079 (1995).MathSciNetCrossRefGoogle Scholar
  19. [KT 96]
    König, J. and Timmer, J. Analyzing x-ray variability by linear state space models. Astronomy and Astrophysics. To appear (1996).Google Scholar
  20. [M 81]
    Miller, R. G. Simultaneous statistical inference. 2nd Edition. Springer Verlag (1981).Google Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • P. J. Bickel
    • 1
  1. 1.Department of StatisticsUniversity of CaliforniaBerkeleyUSA

Personalised recommendations