The Block Empirical Likelihood Method of the Semivarying Coefficient Model with Application to Longitudinal Data

In this article, we consider a semivarying coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to semivarying coefficient longitudinal data model, and prove a nonparametric version of Wilks' theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.