Kernel-based global MLE of partial linear random effects models for longitudinal data

Lei Liu, Zhihua Sun*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Random effects models have been playing a critical role for modelling longitudinal data. However, there are little studies on the kernel-based maximum likelihood method for semiparametric random effects models. In this paper, based on kernel and likelihood methods, we propose a pooled global maximum likelihood method for the partial linear random effects models. The pooled global maximum likelihood method employs the local approximations of the nonparametric function at a group of grid points simultaneously, instead of one point. Gaussian quadrature is used to approximate the integration of likelihood with respect to random effects. The asymptotic properties of the proposed estimators are rigorously studied. Simulation studies are conducted to demonstrate the performance of the proposed approach. We also apply the proposed method to analyse correlated medical costs in the Medical Expenditure Panel Survey data set.

Original languageEnglish (US)
Pages (from-to)615-635
Number of pages21
JournalJournal of Nonparametric Statistics
Volume29
Issue number3
DOIs
StatePublished - Jul 3 2017

Keywords

  • Gaussian quadrature
  • Longitudinal data
  • pooled local maximum likelihood
  • random effects
  • semi-parametric model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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