Gaussian Copula Marginal Regression

By Guido Masarotto, Cristiano Varin. 2012

184 citations recorded by the source corpus.

This paper identifies and develops the class of Gaussian copula models for marginal regression analysis of non-normal dependent observa- tions. 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 like- lihood approach. While the likelihood function is available in closed-form for continuous responses, in the non-continuous setting numerical approx- imations are used. Residual analysis and a specification test are suggested for validating the adequacy of the assumed multivariate model. Methodol- ogy is implemented in a R package called gcmr. Illustrations include simu- lations and real data applications regarding time series, cross-design data, longitudinal studies, survival analysis and spatial regression.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/62156cbd90f1146b249319ebb4f5ca454841fba4

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Citations in the corpus

Count Time Series: A Methodological Review (2021, background): “See Smith and Khaled (2012), Masarotto and Varin (2012), and Han and De Oliveira (2016) for copula techniques in spatial statistics and other settings.”
Beta regression for time series analysis of bounded data, with application to Canada Google Flu Trends. (2014, methodology): “Methods described in the paper are implemented within the more general R [R Core Team (2013)] package gcmr “Gaussian copula marginal regression” [Masarotto and Varin (2012)], version 0.6.1.”
A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls (2014, methodology): “Copula models (Schweizer and Sklar, 1983; Nelsen, 1999) are often used by statisticians (Genest and Favre, 2007; Liu et al., 2012; Masarotto and Varin, 2012) and economists (Chen and Fan, 2006) to study the bivariate and multivariate stochastic dependency among random variables, but they are very…”