Gaussian copula marginal regression
Gaussian copula marginal regression
复制标题
DOI：
10.1214/12-ejs721
复制
发表时间：
2012-01-01
影响因子：
1.1
通讯作者：
Varin, Cristiano
中科院分区：
文献类型：
--
作者：
Masarotto, Guido;Varin, Cristiano
本文识别并开发了一类高斯关联模型，用于非正态相关观测值的边际回归分析。该类提供了具有正常相关误差的传统线性回归模型的自然扩展。任何类型的连续、离散和分类响应都是允许的。依赖性可以根据多元正态误差方便地建模。通过似然法进行推理。虽然似然函数可用于连续响应的封闭形式，但在非连续设置中使用数值近似。建议使用残差分析和规范检验来验证假设的多元模型的充分性。方法是在名为 gcmr 的 R 包中实现的。插图包括有关时间序列、交叉设计数据、纵向研究、生存分析和空间回归的模拟和真实数据应用。
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.