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Activity Number:
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413
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #300685 |
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Title:
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Design Consistent Estimators for a Mixed Linear Model on Survey Data
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Author(s):
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Rong Huang*+ and Michael A. Hidiroglou
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Companies:
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Statistics Canada and Statistics Canada
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Address:
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11k, RH Coats, Ottawa, ON, K1Y 4M2, Canada
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Keywords:
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survey weights ; design consistent ; mixed linear model ; estimating equation ; covariance components ; random effects
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Abstract:
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Most of investigations associated with large government surveys typically require statistical analysis for populations that have a complex hierarchical structure. Classical analyses often fail to account for the nature of complex sampling designs and possibly result in biased estimators 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. We develop a general method for estimating the parameters of the linear mixed model accounting for sampling designs. We obtain the best linear unbiased estimators for the fixed and random effects by solving sample estimating equations. The use of survey weights results in design consistent estimation. We also derived estimators for variance components for the nested error linear regression model. We compared the efficiency of the proposed estimators with that of existing estimators using a simulation study. This simulation study was carried out using a two stage sampling design. Several informative or noninformative sampling schemes were considered in the simulation.
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