  In this article we propose a new variable selection method for analyzing data
collected from longitudinal sample surveys. The procedure is based on the
survey-weighted quadratic inference function, which was recently introduced as
an alternative to the survey-weighted generalized estimating function. Under
the joint model-design framework, we introduce the penalized survey-weighted
quadratic inference estimator and obtain sufficient conditions for the
existence, weak consistency, sparsity and asymptotic normality. To illustrate
the finite sample performance of the model selection procedure, we include a
limited simulation study.
