Electronic Journal of Statistics

Gaussian copula marginal regression

Guido Masarotto and Cristiano Varin

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This paper identifies and develops the class of Gaussian copula models for marginal regression analysis of non-normal dependent observations. The class provides a natural extension of traditional linear regression models with normal correlated errors. Any kind of continuous, discrete and categorical responses is allowed. Dependence is conveniently modelled in terms of multivariate normal errors. Inference is performed through a likelihood approach. While the likelihood function is available in closed-form for continuous responses, in the non-continuous setting numerical approximations are used. Residual analysis and a specification test are suggested for validating the adequacy of the assumed multivariate model. Methodology is implemented in a R package called gcmr. Illustrations include simulations and real data applications regarding time series, cross-design data, longitudinal studies, survival analysis and spatial regression.

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Electron. J. Statist., Volume 6 (2012), 1517-1549.

First available in Project Euclid: 31 August 2012

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Discrete time series Gaussian copula generalized estimating equations likelihood Inference longitudinal data marginal regression multivariate probit spatial data survival data


Masarotto, Guido; Varin, Cristiano. Gaussian copula marginal regression. Electron. J. Statist. 6 (2012), 1517--1549. doi:10.1214/12-EJS721. https://projecteuclid.org/euclid.ejs/1346421603

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