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• W2184528863 abstract "Some investigations associated with large government surveys typically require statistical analysis for populations that have a complex hierarchical structure. Classical analysis often fail to account for the nature of complex sampling designs and possibly result in incorrect inference for the parameters of interest. Linear mixed models can be used to analyze survey data collected from such populations in order to incorporate the complex hierarchical design structure. In this paper, we develop a method for estimating the parameters of the linear mixed model accounting for such sampling designs. We obtain the pseudo best linear unbiased estimators for the fixed and random effects by solving weighted sample estimating equations. The use of survey weights results in design consistent estimation. We also derive estimators for variance components for the nested error linear regression model. We compare the efficiency of the proposed estimators with that of existing estimators using a simulation study. This simulation study uses a two stage sampling design. Several informative or non-informative sampling schemes are considered in the simulation." @default.
• W2184528863 created "2016-06-24" @default.
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• W2184528863 date "2003-01-01" @default.
• W2184528863 modified "2023-09-24" @default.
• W2184528863 title "Design Consistent Estimators for a Mixed Linear Model on Survey Data" @default.
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