Order selection tests with multiply imputed data

Computational Statistics and Data Analysis

Volumn 54, Issue 10, 2010, Pages 2284-2295

  Consentino, Fabrizio ^a   Claeskens, Gerda ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

Akaike information criterion; Hypothesis test; Lack of fit test; Missing data; Multiple imputation; Omnibus test; Order selection

Indexed keywords

AKAIKE INFORMATION CRITERION; HYPOTHESIS TESTS; LACK-OF-FIT TEST; MISSING DATA; MULTIPLE IMPUTATION; ORDER SELECTION;

DATA REDUCTION;

STATISTICAL TESTS;

EID: 77955282117     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2010.04.009     Document Type: Article

References (22)

1. M. Aerts, G. Claeskens, and J.D. Hart Testing the fit of a parametric function Journal of the American Statistical Association 94 1999 869 879
2. M. Aerts, G. Claeskens, and J.D. Hart Testing lack of fit in multiple regression Biometrika 87 2000 405 424
3. H. Akaike Information theory and an extension of the maximum likelihood principle B. Petrov, F. Cski, Second International Symposium on Information Theory 1973 Akadmiai Kiad Budapest pp. 267281
4. N. Bissantz, G. Claeskens, H. Holzmann, and A. Munk Testing for lack of fit in inverse regression with applications to biophotonic imaging Journal of the Royal Statistical Society, Series B 71 1 2009 25 48
5. G. Claeskens, and N.L. Hjort Goodness of fit via nonparametric likelihood ratios Scandinavian Journal of Statistics 31 2004 487 513
6. B. Efron Missing data, imputation, and the bootstrap Journal of the American Statistical Association 89 426 1994 463 479
7. R.L. Eubank, and J.D. Hart Testing goodness-of-fit in regression via order selection criteria The Annals of Statistics 20 1992 1412 1425
8. W. Gonzlez-Manteiga, and A. Prez-Gonzlez Goodness-of-fit tests for linear regression models with missing response data The Canadian Journal of Statistics 34 1 2006 149 170
9. P. Hall, and S.R. Wilson Two guidelines for bootstrap hypothesis testing Biometrics 47 2 1991 757 762
10. J.D. Hart Nonparametric Smoothing and Lack-of-fit Tests 1997 Springer-Verlag New York
11. N.J. Horton, and K.P. Kleinman Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models The American Statistician 61 1 2007 79 90
12. N.J. Horton, and S.R. Lipsitz Multiple imputation in practice: comparison of software packages for regression models with missing variables The American Statistician 55 3 2001 244 254
13. T. Ledwina Data-driven version of Neyman's smooth test of fit Journal of the American Statistical Association 89 1994 1000 1005
14. K.H. Li, X.-L. Meng, T.E. Raghunathan, and D.B. Rubin Significance levels from repeated p -values with multiply-imputed data Statistica Sinica 1 1 1991 65 92
15. K.H. Li, T.E. Raghunathan, and D.B. Rubin Large-sample significance levels from multiply imputed data using moment-based statistics and an F reference distribution Journal of the American Statistical Association 86 416 1991 1065 1073
16. R.J.A. Little, and D.B. Rubin Statistical Analysis with Missing Data second ed. Wiley Series in Probability and Statistics 2002 Wiley-Interscience [John Wiley & Sons] Hoboken, NJ
17. X.-L. Meng, and D.B. Rubin Performing likelihood ratio tests with multiply-imputed data sets Biometrika 79 1 1992 103 111
18. J.P. Reiter Small-sample degrees of freedom for multi-component significance tests for multiple imputation for missing data Biometrika 94 2 2007 502 508
19. D.B. Rubin Missing data, imputation, and the bootstrap: comment Journal of the American Statistical Association 89 426 1994 475 478
20. Schomaker, M., Heumann, C., Toutenburg, H., 2007. New approaches for model selection under missing data, Technical Report, Department of Statistics, Ludwig-Maximilians-Universitt Mnchen.
21. Schomaker, M., Wan, A.T., Heumann, C., 2010. Frequentist model averaging with missing observations, Computational Statistics and Data Analysis x(x), in press (doi:10.1016/j.csda.2009.07.023 ).
22. X. Yang, T.R. Belin, and W.J. Boscardin Imputation and variable selection in linear regression models with missing covariates Biometrics 61 2 2005 498 506