Estimation and inference of the joint conditional distribution for multivariate longitudinal data using nonparametric copulas

Minjung Kwak

Journal of Nonparametric Statistics, 2017, vol. 29, issue 3, 491-514

Abstract: In this paper we study estimating the joint conditional distributions of multivariate 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. Our models and estimation method can be applied in many situations where the conditional mean-based models are not good enough. Empirical copulas combined with time-varying transformation models may allow quite flexible modelling for the joint conditional distributions for multivariate longitudinal data. We derive the asymptotic properties for the copula-based estimators of the joint conditional distribution functions. For illustration we apply our estimation method to an epidemiological study of childhood growth and blood pressure.

Date: 2017
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DOI: 10.1080/10485252.2017.1324966

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