Improved transformed deviance statistic for testing a logistic regression model


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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/47351

Title:	Improved transformed deviance statistic for testing a logistic regression model
Authors:	Taneichi, Nobuhiro Browse this author
	Sekiya, Yuri Browse this author
	Toyama, Jun Browse this author →KAKEN DB
Keywords:	Bartlett adjustment
	Deviance
	Edgeworth expansion
	Logistic regression
Issue Date:	Oct-2011
Publisher:	Elsevier
Journal Title:	Journal of Multivariate Analysis
Volume:	102
Issue:	9
Start Page:	1263
End Page:	1279
Publisher DOI:	10.1016/j.jmva.2011.04.010
Abstract:	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˜ that improves the speed of convergence to the chi-square limiting distribution of D. By numerical comparison, we find that the transformed statistic D˜ performs much better than D. We also give a real data example of D˜ being more reliable than D for testing a hypothesis.
Type:	article (author version)
URI:	http://hdl.handle.net/2115/47351
Appears in Collections:	情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 外山淳

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