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A simulation study of a nested sequence of binomial regression models

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Publication Date
2007
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Pardo Llorente, María del Carmen
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Taylor & Francis
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The inference problem we consider is that of model choice from a nested sequence of binomial regression models. The approach we take is to test successively, from most general to most specific, the corresponding sequence of composite hypotheses. This approach is based on the very general class of divergence measures, the phi-divergence. An approximation to the power function of the new family of test statistics proposed is obtained. An extensive simulation study is carried out by obtaining new test statistics that are a good alternative to the traditional loglikelihood test statistic.
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Nelder, J.A. andWedderburn, R.W.M., 1972, Generalized linear models. Journal of the Royal Statistical Society, A135, 370–384. Agresti, A., 1996, Analysis of Ordinal Categorical Data (NewYork:Wiley). Andersen, E.B., 1997, Introduction to the Statistical Analysis of Categorical Data I (Berlin: Springer). Liu, I. and Agresti, A., 2005, The analysis of ordered categorical data: an overview and a survey of recent developments. Test, 14(1), 1–73. Kullback, S., 1985, Kullback information. In: S. Kotz and N.L. Johnson (Eds) Encyclopedia of Statistical Sciences, Vol. 4 (NewYork: JohnWiley), pp. 421–425. Csiszár, I., 1963, Eine Informationtheorestiche Ungleichung und ihreAnwendung anf den Beweis der Ergodizität Markoffshen Ketten. Publications of the Mathematical Institute of Hungarian Academy of Sciences, Series A,8, 84–108. Ali, S.M. and Silvey, S.D., 1966, A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society, Series B, 28(1), 131–142. Pardo, L., 2006, Statistical Inference Based on Divergence Measures. Statistics: Texbooks and Monographs (NewYork: Chapman & Hall/CRC). Vajda, I., 1989, Theory of Statistical Inference and Information (Boston: Kluwer). Cressie, N. and Read, T.R.C., 1984, Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society Series B, 46, 440–464. Read, T.R.C. and Cressie, N.A.C., 1988, Goodness-of-fit Statistics for Discrete Multivariate Data (New York: Springer-Verlag). Pardo, J.A., Pardo, L. and Pardo, M.C., 2005, Minimum φ-divergence estimator in logistic regression models. Statistical Papers, 47, 91–108. Pardo, J.A., Pardo, L. and Pardo, M.C., 2006, Testing in logistic regression models based on φ-divergence measure. Journal of Statistical Planning and Inference, 136, 982–1006. Cressie, N. and Pardo, L., 2000, Minimum φ-divergence estimator and hierarchical testing in loglinear models. Statistica Sinica, 10, 867–884. Cressie, N., Pardo, L. and Pardo, M.C., 2003, Size and power considerations for testing loglinear models using φ-divergence test statistics. Statistica Sinica, 17(5), 555–570. Searle, S.R., 1971, Linear Models (NewYork: JohnWiley & Sons). Ferguson, T.S., 1996, A Course in Large Sample Theory (NewYork: JohnWiley & Sons). Dale, J.R., 1986, Asymptotic normality of goodness-of-fit statistics for sparse product multinomials. Journal of the Royal Statistical Society, Series B, 41, 48–59.
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