Model assessment tools for a model false world

Statistical Science

Volumn 24, Issue 3, 2009, Pages 303-318

  Lindsay, Bruce ^a   Liu, Jiawei ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b GEORGIA STATE UNIVERSITY   (United States)

Author keywords

Bootstrap; Model credibility index; Model selection; Normality; Statistical distance

Indexed keywords

EID: 77955145381     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/09-STS302     Document Type: Article

References (23)

1. Agresti, A. (2002). Categorical Data Analysis, 2nd ed. Wiley, New York.
2. BERKSON, J. (1938). Some difficulties of interpretation encounted in the application of the chi-square test. J. Amer. Statist. Assoc. 33 526-536.
3. BLOM, G. (1976). Some properties of incomplete U-statistics. Biometrika 63 573-580.
4. BOX, G. E. P. (1976). Science and statistics. J. Amer. Statist. Assoc. 71 791-799.
5. CLAESKENS, G. and HJORT, N. L. (2003). The focused information criterion. With discussions and a rejoinder by the authors. J. Amer. Statist. Assoc. 98 900-945.
6. CRAMÉR, H. (1946). Mathematical Methods of Statistics. Princeton Univ. Press, Princeton, NJ.
7. DAVIES, R. B. (2002). Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case. Biometrika 89 484-489.
8. DAVIES, P. L. (1995). Data features. Statist. Neerlandica 49 185-245.
9. DETTE, H. and MUNK, A. (2003). Some methodological aspects of validation of models in nonparametric regression. Statist. Neerlandica 57 207-244.
10. DIACONIS, P. and EFRON, B. (1985). Reply to comments on "Testing for independence in a two-way table: New interpretations of the chi-square statistic." Ann. Statist. 13 905-913.
11. DONOHO, D. L. (1988). One-sided inference about functionals of a density. Ann. Statist. 16 1390-1420.
12. FERGUSON, T. S. (1996). A Course in Large Sample Theory. Texts in Statistical Science Series. Chapman and Hall/CRC Press, Boca Raton, FL.
13. FREITAG, G. and MUNK, A. (2005). On Hadamard differentiability in k-sample semiparametric models-with applications to the assessment of structural relationships. J. Multivariate Anal. 94 123-158.
14. GHOSH, J. K. and SAMANTA, T. (2001). Model selection-An overview. Current Sci. 80 1135-1144.
15. GOUTIS, C. and ROBERT, C. P. (1998). Model choice in generalised linear models: A Bayesian approach via Kullback-Leibler projections. Biometrika 85 29-37.
16. HODGES, J. L. and LEHMANN, E. L. (1954). Testing the approximate validity of statistical hypotheses. J. Roy. Statist. Soc. Ser. B 16 261-268.
17. LEHMANN, E. L. (1999). Elements of Large-Sample Theory. Springer, New York.
18. LIU, J. and LINDSAY, B. G. (2009). Building and using semiparametric tolerance regions for parametric multinomial models. Ann. Statist. 37 3644-3659.
19. POLITIS, D. N., ROMANO, J. P. andWOLF, M. (1999). Subsampling. Springer, New York.
20. SERFLING, R. J. (1980). Approximation Theorems of Mathematical Statistics. Wiley, New York.
21. SHAPIRO, S. S. andWILK, M. B. (1965). An analysis of variance test for normality: Complete samples. Biometrika 52 591-611.
22. SHAPIRO, S. S.,WILK, M. B. andCHEN, H. J. (1965). A comparative study of various tests for normality. J. Amer. Statist. Assoc. 63 1343-1373.
23. SNEE, R. D. (1974). Graphical display of two-way contingency tables. Amer. Statist. 28 9-12.