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, 15:1

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Mixed model prediction and small area estimation

  • Jiming JiangAffiliated withDepartment of Statistics, University of California Email author 
  • , P. LahiriAffiliated withJoint Program in Survey Methodology, University of Maryland

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Abstract

Over the last three decades, mixed models have been frequently used in a wide range of small area applications. Such models offer great flexibilities in combining information from various sources, and thus are well suited for solving most small area estimation problems. The present article reviews major research developments in the classical inferential approach for linear and generalized linear mixed models that are relevant to different issues concerning small area estimation and related problems.

Key Words

Benchmarking borrowing strength design-consistency mean squared errors empirical Bayes EBLUP generalized linear mixed models higher order asymptotics resampling methods sample surveys variance components

AMS subject classification

62C12 62C25 62G09 62D05 62F11 62F15