An Improved Goodness-of-Fit Test for Logistic Regression Models Based on Case-Control Data by Random Partition
Authors: Deng, Xin1; Wan, Shuwen2; Zhang, Biao3
Source: Communications in Statistics: Simulation and Computation, Volume 38, Number 2, February 2009 , pp. 233-243(11)
Publisher: Taylor and Francis Ltd
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Abstract:
Zhang (1999) proposed a chi-squared goodness-of-fit test for logistic regression models based on case-control data by adapting the Nikulin-Rao-Robson-Moore test. The statistic proposed by Zhang requires the partition of covariate space and the cutoff points for partition are assumed to be known and fixed by experience. Due of lack of a uniform rule for choosing appropriate cutoff points, we propose a data-driven strategy for grouping data and a new statistic for testing logistic regression models based on case-control data. The proposed statistic has an asymptotic chi-squared distribution. Our simulation results show that the proposed statistic is more powerful than the one proposed by Zhang. Application of the proposed statistic to two real datasets is also presented.Keywords: Case-control data; Density ratio model; Empirical likelihood; Goodness-of-fit; Logistic regression
Document Type: Research article
DOI: 10.1080/03610910802460754
Affiliations: 1: PRA International, Lenexa, Kansas, USA 2: School of Life Sciences, Nanjing University, Nanjing, China 3: Department of Mathematics, University of Toledo, Toledo, Ohio, USA
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