We consider the problem of selecting the fixed and random effects in a
mixed linear model. Two kinds of selection problems are considered. The
first is to select the fixed covariates from a set of candidate predictors
when the random effects are not subject to selection; the second is to
select both the fixed covariates and the random effect factors. Our
selection criteria are similar to the generalized information
criterion (GIC), but we show that a naive GIC does not work for the
second kind of selection problem. Asymptotic theory is developed in which
we give sufficient conditions for consistency of the selection criteria
proposed. Finite sample performance of the selection procedures are
investigated by simulation studies.
This work is joint with J. Sunil Rao of Depeartment of
Epidemiology and Biostatistics at CWRU.
Questions? Jiming Jiang