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Selection tests for possibly misspecified hierarchical multinomial marginal models

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  • Colombi, Roberto

Abstract

Hierarchical marginal models have been proposed for categorical data to overcome some limitations of the log-linear approach in modeling marginal distributions. These models can easily satisfy marginal conditional independence conditions and describe with great flexibility the dependence of marginal distributions on covariates. As the richness of the family of hierarchical marginal models leads to comparing models that do not satisfy a nesting relationship, statistical tests for model selection from non-nested, possibly misspecified marginal models are introduced.

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  • Colombi, Roberto, 2020. "Selection tests for possibly misspecified hierarchical multinomial marginal models," Econometrics and Statistics, Elsevier, vol. 16(C), pages 136-147.
    • Handle: RePEc:eee:ecosta:v:16:y:2020:i:c:p:136-147
    DOI: 10.1016/j.ecosta.2019.06.002
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    References listed on IDEAS

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    7. Roberto Colombi & Sabrina Giordano, 2019. "Likelihood-based tests for a class of misspecified finite mixture models for ordinal categorical data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1175-1202, December.
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    1. Seri, Raffaello, 2022. "Computing the asymptotic distribution of second-order U- and V-statistics," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

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