期刊
ELECTRONIC JOURNAL OF STATISTICS
卷 6, 期 -, 页码 1517-1549出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/12-EJS721
关键词
Discrete time series; Gaussian copula; generalized estimating equations; likelihood Inference; longitudinal data; marginal regression; multivariate probit; spatial data; survival data
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据