Abstract
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 language | English |
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Pages (from-to) | 247-255 |
Number of pages | 9 |
Journal | Journal of Statistical Computation and Simulation |
Volume | 40 |
Issue number | 3-4 |
DOIs | |
Publication status | Published - 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