A goodness-of-fit test for logistic-normal models using nonparametric smoothing method

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Title:	A goodness-of-fit test for logistic-normal models using nonparametric smoothing method
Authors:	Lin, Kuo-Chin;Chen, Yi-Ju
Contributors:	淡江大學統計學系
Keywords:	Goodness-of-fit;Logistic-normal models;Longitudinal binary data;Nonparametric smoothing
Date:	2011-02
Issue Date:	2013-06-13 11:27:52 (UTC+8)
Publisher:	Amsterdam: Elsevier BV * North-Holland
Abstract:	Logistic-normal models can be applied for analysis of longitudinal binary data. The aim of this article is to propose a goodness-of-fit test using nonparametric smoothing techniques for checking the adequacy of logistic-normal models. Moreover, the leave-one-out cross-validation method for selecting the suitable bandwidth is developed. The quadratic form of the proposed test statistic based on smoothing residuals provides a global measure for checking the model with categorical and continuous covariates. The formulae of expectation and variance of the proposed statistics are derived, and their asymptotic distribution is approximated by a scaled chi-squared distribution. The power performance of the proposed test for detecting the interaction term or the squared term of continuous covariates is examined by simulation studies. A longitudinal dataset is utilized to illustrate the application of the proposed test.
Relation:	Journal of Statistical Planning and Inference 141(2), pp.1069-1076
DOI:	10.1016/j.jspi.2010.09.016
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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