Logistic Regression Type Small Area Estimations Based on Relative Error

Hwang, Hee-Jin;Shin, Key-Il

  • Received : 20110100
  • Accepted : 20110300
  • Published : 2011.06.30


Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.


Shrinkage estimator;mean squared error;logistic mixed model;logistic regression model


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