Asymptotic null distribution of the modified likelihood ratio test for homogeneity in finite mixture models

Tony S.T. Wong, Kwok Fai Lam and Victoria X. Zhao

Computational Statistics & Data Analysis, 2018, vol. 127, issue C, 248-257

Abstract: Likelihood-based methods play a central role in statistical inference for parametric models. Among these, the modified likelihood ratio test is preferred in testing for homogeneity in finite mixture models. The test statistic is related to the maximum of a quadratic function under general regularity conditions. Re-parameterization is shown to have overcome the difficulty when linear independence is not satisfied. Models with parameter constraints are also considered. The asymptotic null distribution of the test statistic is shown to have a chi-bar-squared distribution in both constrained and unconstrained cases. We extend the result to linear models and demonstrate that the chi-bar-squared distribution is also applicable. The general asymptotic result provides a much simpler testing procedure with an exact form of the asymptotic distribution compared to re-sampling approach in the literature. It also offers accurate p-value as shown in simulation. The results are checked by extensive simulation and are supplemented by a breast cancer data example.

Keywords: Chi-bar-squared distribution; Degeneration; Generalized linear models; Linear independence; Negative definiteness (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:127:y:2018:i:c:p:248-257

DOI: 10.1016/j.csda.2018.05.010

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