A bivariate logistic regression model based on latent variables

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A bivariate logistic regression model based on latent variables. / Kristensen, Simon Bang; Bibby, Bo Martin.

I: Statistics in Medicine, Bind 39, Nr. 22, 09.2020, s. 2962-2979.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Kristensen, SB & Bibby, BM 2020, 'A bivariate logistic regression model based on latent variables', Statistics in Medicine, bind 39, nr. 22, s. 2962-2979. https://doi.org/10.1002/sim.8587

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Kristensen, Simon Bang ; Bibby, Bo Martin. / A bivariate logistic regression model based on latent variables. I: Statistics in Medicine. 2020 ; Bind 39, Nr. 22. s. 2962-2979.

Bibtex

@article{6b7c73bef1d64a66ab7ad0d45dcd7c0a,
title = "A bivariate logistic regression model based on latent variables",
abstract = "Bivariate observations of binary and ordinal data arise frequently and require a bivariate modeling approach in cases where one is interested in aspects of the marginal distributions as separate outcomes along with the association between the two. We consider methods for constructing such bivariate models based on latent variables with logistic marginals and propose a model based on the Ali-Mikhail-Haq bivariate logistic distribution. We motivate the model as an extension of that based on the Gumbel type 2 distribution as considered by other authors and as a bivariate extension of the logistic distribution, which preserves certain natural characteristics. Basic properties of the obtained model are studied and the proposed methods are illustrated through analysis of two data sets: a basic science cognitive experiment of visual recognition and awareness and a clinical data set describing assessments of walking disability among multiple sclerosis patients.",
keywords = "correlated Bernoulli variables, generalized linear mixed models, joint mixed models, MULTIPLE-SCLEROSIS, MULTIVARIATE",
author = "Kristensen, {Simon Bang} and Bibby, {Bo Martin}",
year = "2020",
month = sep,
doi = "10.1002/sim.8587",
language = "English",
volume = "39",
pages = "2962--2979",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "JohnWiley & Sons Ltd.",
number = "22",

}

RIS

TY - JOUR

T1 - A bivariate logistic regression model based on latent variables

AU - Kristensen, Simon Bang

AU - Bibby, Bo Martin

PY - 2020/9

Y1 - 2020/9

N2 - Bivariate observations of binary and ordinal data arise frequently and require a bivariate modeling approach in cases where one is interested in aspects of the marginal distributions as separate outcomes along with the association between the two. We consider methods for constructing such bivariate models based on latent variables with logistic marginals and propose a model based on the Ali-Mikhail-Haq bivariate logistic distribution. We motivate the model as an extension of that based on the Gumbel type 2 distribution as considered by other authors and as a bivariate extension of the logistic distribution, which preserves certain natural characteristics. Basic properties of the obtained model are studied and the proposed methods are illustrated through analysis of two data sets: a basic science cognitive experiment of visual recognition and awareness and a clinical data set describing assessments of walking disability among multiple sclerosis patients.

AB - Bivariate observations of binary and ordinal data arise frequently and require a bivariate modeling approach in cases where one is interested in aspects of the marginal distributions as separate outcomes along with the association between the two. We consider methods for constructing such bivariate models based on latent variables with logistic marginals and propose a model based on the Ali-Mikhail-Haq bivariate logistic distribution. We motivate the model as an extension of that based on the Gumbel type 2 distribution as considered by other authors and as a bivariate extension of the logistic distribution, which preserves certain natural characteristics. Basic properties of the obtained model are studied and the proposed methods are illustrated through analysis of two data sets: a basic science cognitive experiment of visual recognition and awareness and a clinical data set describing assessments of walking disability among multiple sclerosis patients.

KW - correlated Bernoulli variables

KW - generalized linear mixed models

KW - joint mixed models

KW - MULTIPLE-SCLEROSIS

KW - MULTIVARIATE

UR - http://www.scopus.com/inward/record.url?scp=85088146109&partnerID=8YFLogxK

U2 - 10.1002/sim.8587

DO - 10.1002/sim.8587

M3 - Journal article

C2 - 32678481

AN - SCOPUS:85088146109

VL - 39

SP - 2962

EP - 2979

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 22

ER -