Improved transformed deviance statistic for testing a logistic regression model

JOURNAL OF MULTIVARIATE ANALYSIS

卷:102

Article

Taneichi, Nobuhiro¹  Sekiya, Yuri²  Toyama, Jun³ 

[1] Kagoshima Univ, Grad Sch Sci & Engn, Dept Math & Comp Sci, Kagoshima 8900065, Japan

[2] Hokkaido Univ, Kushiro, Hokkaido 0858580, Japan

[3] Hokkaido Univ, Grad Sch Informat Sci & Technol, Div Comp Sci, Sapporo, Hokkaido 0600814, Japan

关键词: Bartlett adjustment;    Deviance;    Edgeworth expansion;    Logistic regression;   

DOI  :  10.1016/j.jmva.2011.04.010

来源: Elsevier

【 摘 要 】

In logistic regression models, we consider the deviance statistic (the log likelihood ratio statistic) D as a goodness-of-fit test statistic. In this paper, we show the derivation of an expression of asymptotic expansion for the distribution of D under a null hypothesis. Using the continuous term of the expression, we obtain a Bartlett-type transformed statistic (D) over tilde that improves the speed of convergence to the chi-square limiting distribution of D. By numerical comparison, we find that the transformed statistic (D) over tilde performs much better than D. We also give a real data example of (D) over tilde being more reliable than D for testing a hypothesis. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

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