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Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response

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  • Javier Espinosa-Brito
  • Christian Hennig

Abstract

The proportional odds cumulative logit model (POCLM) is a standard regression model for an ordinal response. Ordinality of predictors can be incorporated by monotonicity constraints for the corresponding parameters. It is shown that estimators defined by optimization, such as maximum likelihood estimators, for an unconstrained model and for parameters in the interior set of the parameter space of a constrained model are asymptotically equivalent. This is used in order to derive asymptotic confidence regions and tests for the constrained model, involving simple modifications for finite samples. The finite sample coverage probability of the confidence regions is investigated by simulation. Tests concern the effect of individual variables, monotonicity, and a specified monotonicity direction. The methodology is applied on real data related to the assessment of school performance.

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  • Javier Espinosa-Brito & Christian Hennig, 2021. "Inference for the proportional odds cumulative logit model with monotonicity constraints for ordinal predictors and ordinal response," Papers 2107.04946, arXiv.org, revised Jun 2023.
    • Handle: RePEc:arx:papers:2107.04946
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    3. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
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