An omnibus lack of fit test in logistic regression with sparse data

Ying Liu (), Paul Nelson and Shie-Shien Yang

Statistical Methods & Applications, 2012, vol. 21, issue 4, 437-452

Abstract: The usefulness of logistic regression depends to a great extent on the correct specification of the relation between a binary response and characteristics of the unit on which the response is recoded. Currently used methods for testing for misspecification (lack of fit) of a proposed logistic regression model do not perform well when a data set contains almost as many distinct covariate vectors as experimental units, a condition referred to as sparsity. A new algorithm for grouping sparse data to create pseudo replicates and using them to test for lack of fit is developed. A simulation study illustrates settings in which the new test is superior to existing ones. Analysis of a dataset consisting of the ages of menarche of Warsaw girls is also used to compare the new and existing lack of fit tests. Copyright Springer-Verlag 2012

Keywords: Model building; Binary response; Pseudo replicates; Type I error; Power (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s10260-012-0197-0

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