Joint Regression Analysis of Correlated Data Using Gaussian Copulas
This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correla...
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| Veröffentlicht in: | Biometrics 2009-03, Vol.65 (1), p.60-68 |
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| container_end_page | 68 |
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| container_title | Biometrics |
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| creator | Song, Peter X.-K Li, Mingyao Yuan, Ying |
| description | This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correlated outcomes. This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients. The availability of joint probability models enables us to develop a full maximum likelihood inference. Numerical illustrations are focused on regression models for discrete correlated data, including multidimensional logistic regression models and a joint model for mixed normal and binary outcomes. In the simulation studies, the proposed copula-based joint model is compared to the popular generalized estimating equations, which is a moment-based estimating equation method to join univariate GLMs. Two real-world data examples are used in the illustration. |
| doi_str_mv | 10.1111/j.1541-0420.2008.01058.x |
| format | Article |
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| ispartof | Biometrics, 2009-03, Vol.65 (1), p.60-68 |
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| source | MEDLINE; JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current); Wiley Online Library All Journals |
| subjects | Biometric Methodology Biometrics Biometry - methods Correlated data Correlations Covariance matrices Data analysis Dispersion models Estimators Gaussian copula GEEs Inference Linear Models Maximum likelihood estimation Mixed outcomes Modeling Parametric models Regression Analysis Simulation Statistics as Topic - methods Theory |
| title | Joint Regression Analysis of Correlated Data Using Gaussian Copulas |
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