Statistics and Its Interface

Volume 4 (2011)

Number 1

A score test for variance components in a semiparametric mixed-effects model under non-normality

Pages: 65 – 72

DOI: https://dx.doi.org/10.4310/SII.2011.v4.n1.a7

Authors

Yan Sun (School of Economics, Shanghai University of Finance and Economics, Shanghai, China)

Jin-Ting Zhang (Department of Statistics and Applied Probability, National University of Singapore)

Abstract

In this paper, we propose a score test for variance components in a semiparametric mixed-effects model when the random-effects and measurement errors are not normally distributed. The asymptotic null distribution of the test statistic is shown to be a simple chi-squared distribution with the degrees of freedom being the number of linearlyindependent variance components. The simulation results show that the proposed score test is robust against the nonnormality of the random-effects and the measurement errors and performs well in terms of both size and power. The score test is illustrated via an application to a real longitudinal data set collected in a clinical trial study.

Keywords

extended quasi-likelihood, Laplace approximation, local linear smoothing, score test, semiparametric mixed-effects model, variance components

2010 Mathematics Subject Classification

Primary 62G10, 62G20. Secondary 62G08.

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Published 28 February 2011