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.

翻译：在本条中,我们提出一种新的可变选择方法,用于分析从纵向抽样调查中收集的数据,该程序以调查加权二次推断功能为基础,该功能最近被作为调查加权通用估计功能的替代品引入。在联合模型设计框架下,我们引入了受处罚的调查加权二次推断估计器,并获得足够条件,以证明存在、一致性弱、宽度和无症状的正常性。为了说明模型选择程序的有限抽样性能,我们包括了有限的模拟研究。