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Generalised partial linear single-index mixed models for repeated measures data

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  • Jinsong Chen
  • Inyoung Kim
  • George R. Terrell
  • Lei Liu

Abstract

In this paper, we propose generalised partial linear single-index mixed models for analysing repeated measures data. A penalised quasi-likelihood approach using P-spline is used to estimate the nonparametric function, linear parameters, and single-index coefficients. Asymptotic properties of the estimators are developed when the dimension of spline basis grows with increasing sample size. Simulation examples and two applications: the study of health effects of air pollution in North Carolina, and treatment effect of naltrexone on health costs for alcohol-dependent individuals, illustrate the effectiveness of our approach.

Suggested Citation

  • Jinsong Chen & Inyoung Kim & George R. Terrell & Lei Liu, 2014. "Generalised partial linear single-index mixed models for repeated measures data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(2), pages 291-303, June.
    • Handle: RePEc:taf:gnstxx:v:26:y:2014:i:2:p:291-303
    DOI: 10.1080/10485252.2014.891029
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    Cited by:

    1. S. Hossain & S. Ejaz Ahmed & Grace Y. Yi & B. Chen, 2016. "Shrinkage and pretest estimators for longitudinal data analysis under partially linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(3), pages 531-549, September.

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