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 |
|---|---|
| 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 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics
Cite this
}
Variable selection in a linear growth curve model with autoregressive within-individual errors. / Sohn, So Young.
In: Journal of Statistical Computation and Simulation, Vol. 40, No. 3-4, 01.04.1992, p. 247-255.Research output: Contribution to journal › Article
TY - JOUR
T1 - Variable selection in a linear growth curve model with autoregressive within-individual errors
AU - Sohn, So Young
PY - 1992/4/1
Y1 - 1992/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0344762052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0344762052&partnerID=8YFLogxK
U2 - 10.1080/00949659208811380
DO - 10.1080/00949659208811380
M3 - Article
AN - SCOPUS:0344762052
VL - 40
SP - 247
EP - 255
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
SN - 0094-9655
IS - 3-4
ER -