Multi-level modelling under informative sampling

• 成果类型: Article
• 署名作者: Pfeffermann, Danny; Da Silva Moura, Fernando Antonio; Do Nascimento Silva, Pedro Luis
• 署名单位: Hebrew University of Jerusalem; Universidade Federal do Rio de Janeiro; Escola Nacional de Ciencias Estatisticas (ENCE)
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.4.943
• 发表日期: 2006
• 页码: 943959
• 关键词: probabilities; selection

摘要

We consider a model-dependent approach for multi-level modelling that accounts for informative probability sampling of first- and lower-level population units. The proposed approach consists of first extracting the hierarchical model holding for the sample data given the selected sample, as a function of the corresponding population model and the first- and lower-level sample selection probabilities, and then fitting the resulting sample model using Bayesian methods. An important implication of the use of the model holding for the sample is that the sample selection probabilities feature in the analysis as additional data that possibly strengthen the estimators. A simulation experiment is carried out in order to study the performance of this approach and compare it to the use of 'design-based' methods. The simulation study indicates that both approaches perform in general equally well in terms of point estimation, but the model-dependent approach yields confidence/credibility intervals with better coverage properties. Another simulation study assesses the impact of misspecification of the models assumed for the sample selection probabilities. The use of maximum likelihood estimation is also considered.