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A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses

Author

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  • Francesco BARTOLUCCI

    (Universit… di Perugia, Dipartimento di Economia)

  • Silvia BACCI

    (Universit… di Perugia, Dipartimento di Economia)

  • Claudia PIGINI

    (Universit… Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)

Abstract

An alternative to using normally distributed random effects in modeling clustered binary and ordered responses is based on using a nite-mixture. This approach gives rise to a exible class of generalized linear mixed models for item responses, multilevel data, and longitudinal data. A test of misspeci cation for these finite-mixture models is proposed which is based on the comparison between the Marginal and the Conditional Maximum Likelihood estimates of the fixed effects as in the Hausman's test. The asymptotic distribution of the test statistic is derived; it is of chi-squared type with a number of degrees of freedom equal to the number of covariates that vary within the cluster. It turns out that the test is simple to perform and may also be used to select the number of components of the finite-mixture, when this number is unknown. The approach is illustrated by a series of simulations and three empirical examples covering the main fields of application.

Suggested Citation

  • Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    • Handle: RePEc:anc:wpaper:410
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    References listed on IDEAS

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    1. Shun Yu & Xianzheng Huang, 2017. "Random-intercept misspecification in generalized linear mixed models for binary responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 333-359, August.

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    More about this item

    Keywords

    Generalized Linear Mixed Models; Hausman Test; Item Response Theory; Latent Class model; Longitudinal data; Multilevel data;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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