Fixed and random effects selection in mixed effects models.

@article{Ibrahim2011FixedAR,
  title={Fixed and random effects selection in mixed effects models.},
  author={J. Ibrahim and H. Zhu and Ramon I. Garcia and Ruixin Guo},
  journal={Biometrics},
  year={2011},
  volume={67 2},
  pages={
          495-503
        }
}
We consider selecting both fixed and random effects in a general class of mixed effects models using maximum penalized likelihood (MPL) estimation along with the smoothly clipped absolute deviation (SCAD) and adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions. The MPL estimates are shown to possess consistency and sparsity properties and asymptotic normality. A model selection criterion, called the IC(Q) statistic, is proposed for selecting the penalty… Expand

Tables and Topics from this paper

VARIABLE SELECTION IN LINEAR MIXED EFFECTS MODELS.
Random Effect Selection in Linear Mixed Models.
Fixed effects Selection in high dimensional Linear Mixed Models
Bayesian nonparametric centered random effects models with variable selection.
  • Mingan Yang
  • Mathematics, Medicine
  • Biometrical journal. Biometrische Zeitschrift
  • 2013
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 47 REFERENCES
Joint variable selection for fixed and random effects in linear mixed-effects models.
Random effects selection in linear mixed models.
VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA.
A Note on Conditional AIC for Linear Mixed-Effects Models.
Variable selection for semiparametric mixed models in longitudinal studies.
Adaptive Lasso for Cox's proportional hazards model
Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Generalized score test of homogeneity for mixed effects models
...
1
2
3
4
5
...