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Dependence Modeling

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A joint regression modeling framework for analyzing bivariate binary data in R

Giampiero Marra
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  • Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK
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  • De Gruyter OnlineGoogle Scholar
/ Rosalba Radice
  • Department of Economics, Mathematics and Statistics, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK
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Published Online: 2017-12-07 | DOI: https://doi.org/10.1515/demo-2017-0016

Abstract

We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets.

Keywords: binary data; copula; confounding; joint model; penalized smoother; selection bias; R; simultaneous parameter estimation

MSC 2010: 62H99; 62J02

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About the article

Received: 2017-09-22

Accepted: 2017-10-18

Published Online: 2017-12-07

Published in Print: 2017-12-20


Citation Information: Dependence Modeling, Volume 5, Issue 1, Pages 268–294, ISSN (Online) 2300-2298, DOI: https://doi.org/10.1515/demo-2017-0016.

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© 2017. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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