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Activity Number:
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492
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Type:
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Invited
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Date/Time:
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Wednesday, August 5, 2009 : 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 - #302861 |
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Title:
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Robust Small Area Estimation Using Penalized Spline Mixed Models
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Author(s):
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J.N.K. Rao*+ and Sanjoy K. Sinha
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Companies:
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Carleton University and Carleton University
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Address:
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School of Mathematics and Statistics, Ottawa, ON, K2G 4H8, Canada
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Keywords:
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bootstrap ; mean squared prediction error ; outliers ; random effects ; small area mean ; unit level model
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Abstract:
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Small area estimation has been extensively studied under linear mixed models. In particular, empirical best linear unbiased prediction (EBLUP) estimators of small area means and associated estimators of mean squared prediction error (MSPE) that are nearly unbiased have been developed. However, EBLUP estimators can be sensitive to outliers. Sinha and Rao (2007) developed a robust EBLUP method and demonstrated its advantages over EBLUP under a unit level linear mixed model in the presence of outliers in the random small area effects and/or unit level errors. A bootstrap method of estimating MSPE of the robust EBLUP was also proposed. In this paper, we relax the assumption of linear regression for the fixed part of the model and replace it by a weaker assumption of a penalized spline regression and develop robust EBLUP estimators. Bootstrap estimators of MSPE are also developed.
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