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Abstract Details

Activity Number: 305
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #305502
Title: A Hierarchical Mixture Model for Small-Area Estimation
Author(s): Adrijo Chakraborty*+ and Gauri Datta and Abhyuday Mandal
Companies: University of Georgia and University of Georgia and University of Georgia
Address: 101 Cedar Street, Athens, GA, 30602, United States
Keywords: Survey Sampling ; Small Area Estimation ; Outliers ; Mixture Models ; Hierarchical Bayes ; Noninformative priors

Model based small area estimation techniques are popular in survey sampling. Normal distribution of the sampling error and random small area effects in a mixed linear model is widely assumed. However, presence of outliers in the survey data could affect the fit of such models and the resulting small area estimates may fail to borrow information from other small areas. In order to account for possible presence of outliers, a Bayesian hierarchical finite scale mixture of normal models is proposed to analyze unit level data. A class of noninformative priors has been proposed for the hyperparameters. Propriety of the resulting posterior distributions under different choices of hyperpriors is studied. Performance of the model is illustrated through a simulation study.

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