A pseudo maximum likelihood approach to multilevel modelling of survey data

被引:23
作者
Kovacevic, MS
Rai, SN
机构
[1] STAT Canada, Social Survey Methods Div, Ottawa, ON K1A 0T6, Canada
[2] St Jude Childrens Res Hosp, Dept Biostat, Memphis, TN 38105 USA
关键词
finite population inference; hierarchical linear modelling; probability sampling; variance components estimation; weighting;
D O I
10.1081/STA-120017802
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
An application of the pseudo maximum likelihood method to estimation of a multilevel linear model fitted to the dependent observations coming from a finite population is demonstrated. The proposed approach provides a closed form solution for estimating of the model parameters. It is computationally simpler than the iterative procedures suggested in the literature (e.g., the iterative probability weighted least squares method of Pfeffermann et al. (Pfeffermann, D., Skinner, C.J., Holmes, D.J., Goldstein, H., Rasbash, J. (1998). Weighting for unequal selection probabilities in multilevel models. Journal of Royal Statistical Society B 60:23-40)). Issues related to model and sample design hierarchies and their impact on estimation are discussed. A problem of weighting at different levels is addressed. A small simulation study showed that the proposed procedure is efficient even for small within group sample sizes.
引用
收藏
页码:103 / 121
页数:19
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