Maximum likelihood estimation and parameter interpretation in elliptical mixed logistic regression

Cristiano C. Santos () and Rosangela H. Loschi ()
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Cristiano C. Santos: Universidade Federal de Minas Gerais
Rosangela H. Loschi: Universidade Federal de Minas Gerais

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 1, No 10, 209-230

Abstract: Abstract We introduce the class of elliptical mixed logistic model with focus on the normal/independent subclass. Parameter interpretation in mixed logistic model is not straightforward since the odds ratio is random. For the proposed models, we obtain the odds ratio distribution and its summaries used to interpret the fixed effects and to measure the heterogeneity among the clusters thus extending previous results. Fisher information is also obtained. A Monte Carlo expectation-maximization algorithm is considered to obtain the maximum likelihood estimates. A simulation study is performed comparing normal and heavy-tailed models. It also address the effect of the misspecification of the random effect distribution and other model aspects in the parameter interpretation. A data analysis is performed showing the utility of heavy-tailed mixed logistic model. Among the main conclusions, we note that the misspecification of the random effect distribution influences the fixed effects interpretation and the quantification of the among clusters heterogeneity.

Keywords: Heavy tail; EM-type algorithm; Random odds ratio; 62J12; 62F10; 62Exx (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11749-016-0507-1

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