The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Shrinkage Prediction for Small Area Estimations

축소예측을 이용한 소지역 추정

  • Hwang, Hee-Jin (Dept. of Statistics, Hankuk University of Foreign Studies) ;
  • Shin, Key-Il (Dept. of Statistics, Hankuk University of Foreign Studies)
  • 황희진 (한국외국어대학교 통계학과) ;
  • 신기일 (한국외국어대학교 정보통계학과)
  • Published : 2008.02.29
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Abstract

Many small area estimation methods have been suggested. Also for the comparison of the estimation methods, model diagnostic checking techniques have been studied. Almost all of the small area estimators were developed by minimizing MSE(Mean square error) and so the MSE is the well-known comparison criterion for superiority. In this paper we suggested a new small area estimator based on minimizing MSPE(Mean square percentage error) which is recently re-highlighted. Also we compared the new suggested estimator with the estimators explained in Shin et al. (2007) using MSE, MSPE and other diagnostic checking criteria.

많은 소지역 추정량이 제안되었으며, 국내외에서 소지역 추정에 관한 많은 연구가 진행되고 있다. 또한 소지역 추정량의 특성과 우수성을 비교하기위한 비교통계량도 연구되고 있다. 기존의 소지역 추정량은 MSE(Mean square error)를 최소화하여 얻어지며, 이에 따라 추정량의 우수성도 MSE를 기준으로 판단하고 있다. 본 논문에서는 최근 새롭게 재조명 되고 있는 MSPE(Mean square percentage error)를 최소화하는 추정량을 제안하였다. 신기일 등 (2007)에서 사용된 비교통계량과 MSE 그리고 MSPB를 이용하여 제안된 추정량과 기존의 소지역 추정량을 비교하였다.

Keywords

References

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Cited by

  1. Shrinkage Small Area Estimation Using a Semiparametric Mixed Model vol.27, pp.4, 2014, https://doi.org/10.5351/KJAS.2014.27.4.605
  2. Logistic Regression Type Small Area Estimations Based on Relative Error vol.24, pp.3, 2011, https://doi.org/10.5351/KJAS.2011.24.3.445
  3. Relative Error Prediction via Penalized Regression vol.28, pp.6, 2015, https://doi.org/10.5351/KJAS.2015.28.6.1103