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

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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
Numero di pagine48
RivistaStatistical Modelling
Stato di pubblicazionePublished - 2018

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Logistic Model
Odds Ratio
Marginal Distribution
Maximum Likelihood Estimation
Penalty
Covariates
Adjacent
Model
Logistic model
Simulation
Odds ratio
Maximum likelihood estimation

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cita questo

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title = "A penalized approach for the bivariate ordered logistic model with applications to social and medical data",
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.",
keywords = "Dale model, bivariate ordered logistic model, ordinal association, penalized maximum likelihood estimation",
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journal = "Statistical Modelling",
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T1 - A penalized approach for the bivariate ordered logistic model with applications to social and medical data

AU - Lovison, Gianfranco

AU - Enea, Marco

AU - Enea, Marco

PY - 2018

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N2 - 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.

AB - 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.

KW - Dale model

KW - bivariate ordered logistic model

KW - ordinal association

KW - penalized maximum likelihood estimation

UR - http://hdl.handle.net/10447/293757

UR - http://smj.sagepub.com/archive/

M3 - Article

JO - Statistical Modelling

JF - Statistical Modelling

SN - 1471-082X

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