Testing non-nested log-linear models with pseudo estimator

Hyun Jip Choi and Chong Sun Hong

Communications in Statistics - Theory and Methods, 2000, vol. 29, issue 7, 1539-1547

Abstract: As the number of random variables for the categorical data increases, the possible number of log-linear models which can be fitted to the data increases rapidly, so that various model selection methods are developed. However, we often found that some models chosen by different selection criteria do not coincide. In this paper, we propose a comparison method to test the final models which are non-nested. The statistic of Cox (1961, 1962) is applied to log-linear models for testing non-nested models, and the Kullback-Leibler measure of closeness (Pesaran 1987) is explored. In log-linear models, pseudo estimators for the expectation and the variance of Cox's statistic are not only derived but also shown to be consistent estimators.

Date: 2000
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DOI: 10.1080/03610920008832561

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