How to choose a working model for measuring the statistical evidence about a regression parameter

International Statistical Review

Volumn 73, Issue 3, 2005, Pages 351-363

  Blume, Jeffrey D ^a  

^a BROWN UNIVERSITY   (United States)

Author keywords

Misleading evidence; Model selection; Statistical evidence; The law of likelihood

Indexed keywords

EID: 29344434483     PISSN: 03067734     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1751-5823.2005.tb00153.x     Document Type: Article

References (21)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. 2nd International Symposium on Information Theory. Eds. B.N. Petrov and F. Csaki. Budapest: Akademiai Kiado.
2. Barnard, G.A. (1947). "Review of Wald, A. Sequential Analysis", Journal of the American Statistical Association, 42, 658-664.
3. Berger, J.O. & Wolpert, R.L. (1988). The likelihood principle. Hayward, California: Institute of Mathematical Statistics.
4. Birnbaum, A. (1962). On the foundations of statistical inference (with discussion). Journal of the American Statistical Association, 53, 259-326.
5. Blume, J.D. (2002). Likelihood methods for measuring statistical evidence. Statistics in Medicine, 21, 2563-2599.
6. Burnham, K.P. & Anderson, D.R. (1998). Model selection and inference: A practical information-theoretic approach. New York: Springer-verlag.
7. Diggle, P.J., Heagerty, P., Liang, K.Y. & Zeger, S.L. (2002). Analysis of longitudinal data (2nd ed). Oxford: Oxford University Press.
8. Hacking, I. (1965). Logic of Statistical Inference. New York: Cambridge University Press.
9. Harrell, F.E. (2001). Regression modeling strategies. New York: Springer-verlag.
10. Montgomery, D. & Peck, E. (1992). Introduction to linear regression analysis. New York, NY: John Wiley & Sons.
11. Neter, J., Wasserman, W. & Kutner, M.H. (1990). Applied linear statistical models (3rd ed). Boston MA: Irwin.
12. Neuhaus, J.M. & Kalbfleisch, J.D. (1998). Between- and within-cluster covariate effects in the analysis of clustered data. Biometrics, 54, 638-645.
13. Pan, W. (2002). A note on the use of marginal likelihood and conditional likelihood in analyzing clustered data. The American Statistician, 56(3), 171-174.
14. Pratt, J.W. (1977). 'Decisions' as statistical evidence and Birnbaum's confidence concept. Synthese, 36, 59-69.
15. Royall, R.M. (1997). Statistical Evidence: A likelihood paradigm. London: Chapman and Hall.
16. Royall, R.M. (2000). On the probability of observing misleading statistical evidence (with discussion). Journal of the American Statistical Association, 95(451), 760-767.
17. Royall, R.M. & Tsou, T.S. (2003). Interpreting statistical evidence using imperfect models: robust adjusted likelihood functions. Journal of Royal Statistical Society, Series B, 65(2), 391-404.
18. Seber, G.A.F. (1977). Linear Regression Analysis. New York: John Wiley & Sons.
19. Smith, C.A.B. (1953). The Detection of Linkage in Human Genetics. Journal of the Royal Statistical Society, Series B, 15, 153-192.
20. Woodroofe, M. (1982). On model selection and the arc sine laws. The Annals of Statistics, 10(4), 1182-1194.
21. Zhang, P. (1992). On the distributional properties of model selection criteria. Journal of the American Statistical Association, 87(419), 732-737.