Statistics and Its Interface

Volume 10 (2017)

Number 3

Varying-coefficient single-index model for longitudinal data

Pages: 495 – 504

DOI: https://dx.doi.org/10.4310/SII.2017.v10.n3.a12

Authors

Hongmei Lin (School of Statistics, East China Normal University, Shanghai, China)

Riquan Zhang (School of Statistics, East China Normal University, Shanghai, China)

Jianhong Shi (School of Mathematics and Computer Science, Shanxi Normal University, Linfen, China)

Yuedong Wang (Department of Statistics and Applied Probability, University of California at Santa Barbara)

Abstract

In this paper we consider a general class of varying-coefficient single-index models for longitudinal data. This class of models provides a tool for simultaneous dimension reduction and the exploration of dynamic patterns. We develop an estimation procedure using Cholesky decomposition, local linear and backfitting technique. Asymptotic normality for the proposed estimators of varying-coefficient functions, link function and parameters will be established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.

Keywords

varying-coefficient single-index models, Cholesky decomposition, local linear regression, longitudinal data

2010 Mathematics Subject Classification

Primary 62G05. Secondary 62E20.

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Published 31 January 2017