Variable selection in a linear growth curve model with autoregressive within-individual errors

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In this paper, we consider variable selection schemes in a linear random coefficient growth curve model. Unbalanced within-individual data is assumed to have first-order linear correlation (ρ). A variable selection scheme suggested in the between-individual model is a weighted stepwise selection. A simulation study is conducted to compare the performances of the maximum likelihood (ML) and the ordinary least square (OLS) estimators in the within-individual regression model in terms of the ability of variable selection in the between-individual model. Simulation results indicate the following: Under the suspicion of high autocorrelation error (ρ > 0.5), the ML estimation is necessary in the within-individual model. When it is believed that the p is as small as 0.1 and the heterogeneity of variance is relatively low then, the OLS estimator can replace the ML estimator in terms of selection performance in the between-individual model.

Original languageEnglish
Pages (from-to)247-255
Number of pages9
JournalJournal of Statistical Computation and Simulation
Issue number3-4
Publication statusPublished - 1992 Apr 1

Bibliographical note

Funding Information:
*This paper was prepared in conjunction with research funded by the Naval Postgraduate School.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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