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A note on bootstrap model selection criterion. (English) Zbl 0843.62050

Summary: We show that a bootstrap model selection criterion constructed by directly plugging-in a consistent estimator in place of the unknown parameter has a downward bias of amount roughly equivalent to the number of parameters in the approximating model. An example is provided to illustrate the points discussed.

MSC:

62G09 Nonparametric statistical resampling methods
Full Text: DOI

References:

[1] Akaike, H., Information theory and an extension of the maximum likelihood principle, (Petrov, B. N.; Csáki, F., Proc. 2nd Internat. Symp. on Information Theory (1973), Akadémia Kiadó: Akadémia Kiadó Budapest), 267-281 · Zbl 0283.62006
[2] Beran, R.; Ducharme, G., Asymptotic Theory for Bootstrap Methods in Statistics (1991), Centre de Recherches Mathématiques, Univ. of Montreal · Zbl 0733.62050
[3] Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 7, 1-26 (1979) · Zbl 0406.62024
[4] Linhart, H.; Zucchini, W., Model Selection (1986), Wiley: Wiley New York · Zbl 0665.62003
[5] Sakamoto, Y.; Ishiguro, M.; Kitagawa, G., Akaike Information Criterion Statistics (1986), KTK Scientific Publishers: KTK Scientific Publishers Tokyo · Zbl 0608.62006
[6] Schall, R.; Zucchini, W., Model selection and the estimation of odds ratios in the presence of extraneous factors, Statist. Med., 9, 1131-1141 (1990)
[7] Shao, J., Linear model selection by cross-validation, J. Amer. Statist. Assoc., 88, 486-494 (1993) · Zbl 0773.62051
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