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
2019-01-01

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.
2019
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].
File in questo prodotto:
File Dimensione Formato  
EneaLovison.pdf

accesso aperto

Descrizione: Articolo principale
Tipologia: Pre-print
Dimensione 517.09 kB
Formato Adobe PDF
517.09 kB Adobe PDF Visualizza/Apri
1471082x18782063.pdf

Solo gestori archvio

Descrizione: Articolo
Tipologia: Versione Editoriale
Dimensione 728.74 kB
Formato Adobe PDF
728.74 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/293757
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact