Your browser doesn't support javascript.
loading
Estimation for volunteer web survey samples using a model-averaging approach.
Liu, Zhan; Zheng, Junbo; Tu, Chaofeng; Pan, Yingli.
Afiliación
  • Liu Z; Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.
  • Zheng J; Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.
  • Tu C; Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.
  • Pan Y; Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China.
J Appl Stat ; 50(16): 3251-3271, 2023.
Article en En | MEDLINE | ID: mdl-37969891
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.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Appl Stat Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Appl Stat Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido