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Estimation of the joint conditional distribution for repeatedly measured bivariate cholesterol data using nonparametric copula
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 Title & Authors
Estimation of the joint conditional distribution for repeatedly measured bivariate cholesterol data using nonparametric copula
Kwak, Minjung;
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We study estimation and inference of the joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. For the estimation of marginal models we consider a class of time-varying transformation models and combine the two marginal models using nonparametric empirical copulas. Regression parameters in the transformation model can be obtained as the solution of estimating equations and our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Nonparametric copulas combined with time-varying transformation models may allow quite flexible modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.
Bivariate longitudinal data;empirical copula;joint conditional distribution;time-varying transformation models;
 Cited by
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