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.

Enea, M., & Lovison, G. (2019). A penalized approach for the bivariate ordered logistic model with applications to social and medical data. STATISTICAL MODELLING, 19(5), 467-500 [10.1177/1471082X18782063].

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

Enea, Marco
;
Lovison, Gianfranco
2019

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.
Settore SECS-S/05 - Statistica Sociale
Settore SECS-S/01 - Statistica
Enea, M., & Lovison, G. (2019). A penalized approach for the bivariate ordered logistic model with applications to social and medical data. STATISTICAL MODELLING, 19(5), 467-500 [10.1177/1471082X18782063].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/293757
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