A penalized approach for the bivariate ordered logistic model with applications to social and medical data

Risultato della ricerca: Articlepeer review

Abstract

Bivariate ordered logistic models (BOLMs) are appealing to jointly model the marginal distribution of two ordered responses and their association, given a set of covariates. When the number of categories of the responses increases, the number of global odds ratios to be estimated also increases, and estimation gets problematic. In this work we propose a non-parametric approach for the maximum likelihood (ML) estimation of a BOLM, wherein penalties to the differences between adjacent row and column effects are applied. Our proposal is then compared to the Goodman and Dale models. Some simulation results as well as analyses of two real data sets are presented and discussed.
Lingua originaleEnglish
pagine (da-a)467-500
Numero di pagine34
RivistaStatistical Modelling
Volume19
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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