Estimation for volunteer web survey samples using a model-averaging approach

作     者：Liu, Zhan Zheng, Junbo Tu, Chaofeng Pan, Yingli 

作者机构：Hubei Univ Sch Math & Stat Hubei Key Lab Appl Math Wuhan 430062 Hubei Peoples R China 

出 版 物：《JOURNAL OF APPLIED STATISTICS》 (应用统计学杂志)

年 卷 期：2023年第50卷第16期

页      面：3251-3271页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：National Social Science Foundation of China [18BTJ022] 

主　　题：Volunteer web survey sample model-averaging approach propensity score logistic regression model generalized boosted model 

摘      要：Propensity score approach is a popular technique for estimating the population based on volunteer web survey samples. Various models have been used to estimate propensity scores and produce different population estimates. To obtain more accurate population estimators, we propose a model-averaging estimation approach based on propensity score estimates from a parametric logistic regression model and a nonparametric generalized boosted model. Consistency and asymptotic normality of the proposed estimators are established. A computation algorithm is also developed to implement the proposed method. Simulation studies are conducted to compare the performance of the proposed method with the other methods. A survey data from the Netizen Social Awareness Survey (NSAS) is used to illustrate the proposed methodology.