Efficient estimation of longitudinal data additive varying coefficient regression models

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Abstract

We consider a longitudinal data additive varying coefficient regression model, in which the coefficients of some factors (covariates) are additive functions of other factors, so that the interactions between different factors can be taken into account effectively. By considering within-subject correlation among repeated measurements over time and additive structure, we propose a feasible weighted two-stage local quasi-likelihood estimation. In the first stage, we construct initial estimators of the additive component functions by B-spline series approximation. With the initial estimators, we transform the additive varying coefficients regression model into a varying coefficients regression model and further apply the local weighted quasi-likelihood method to estimate the varying coefficient functions in the second stage. The resulting second stage estimators are computationally expedient and intuitively appealing. They also have the advantages of higher asymptotic efficiency than those neglecting the correlation structure, and an oracle property in the sense that the asymptotic property of each additive component is the same as if the other components were known with certainty. Simulation studies are conducted to demonstrate finite sample behaviors of the proposed estimators, and a real data example is given to illustrate the usefulness of the proposed methodology.

Keywords

additive vary-coefficient model longitudinal data modified Cholesky decomposition within-subject correlation 

2000 MR Subject Classification

62H12 62G08 62G20 62F12 
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Copyright information

© Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina
  2. 2.School of Statistics and ManagementShanghai University of Finance and EconomicsShanghaiChina

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