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Hierarchical Bayesian Estimation under a Log-Transformed Small Area Model with Measurement Error Covariate
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공변량의 측정오차를 고려한 로그 변환된 소지역 모델의 계층적 베이즈 추정

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Type
Academic journal
Author
Huijun Kim (대구대학교 대학원 통계학과) Jinseub Hwang (대구대학교)
Journal
The Korean Data Analysis Society Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제24권 제2호 KCI Accredited Journals
Published
2022.4
Pages
573 - 584 (12page)
DOI
10.37727/jkdas.2022.24.2.573

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Hierarchical Bayesian Estimation under a Log-Transformed Small Area Model with Measurement Error Covariate
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Research history (2)

2022

Hierarchical Bayesian Estimation under a Log-Transformed Small Area Model with Measurement Error Covariate
Huijun Kim ,  Jinseub Hwang Journal of The Korean Data Analysis Society 2022.04 Academic journal
Hierarchical Bayes Estimation under a Log-Transformed Small Area Model with Measurement Error Covariate
김희준 통계학과 2022.01 Thesis
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Previous researches proposed various extended versions of the Fay-Herriot model to estimate a small area means. Especially, we developed extended models that could take into account covariate measurement errors along with various distribution of outcome variables(normal distribution, binomial distribution, and Poisson distribution). Chandra, Aditya, Kumar(2018) proposed a log-transformed small area model that could take into consideration outcome variables in asymmetric forms rather than normal distribution bigger than 0, but this model can not consider covariate measurement errors. In this study, we extend the log-transformed small area model that could take into account measurement errors of covariates. The measurement error model is divided into a functional measurement error model that assumes non-stochastic on the true value of covariates and a structural measurement error model that assumes stochasticity. In this study, a structural measurement error model was considered. Hierarchical Bayesian estimation is used, which is based on Gibbs sampling as a method of MCMC(Markov chain Monte Carlo), to estimate model fitting and parameters. The conditional distribution of all parameters is calculated for Gibbs sampling and simulation studies are carried out to check the performance of the models. In addition, an application analysis is conducted based on the 2010 National Health Nutrition survey data which is a national sample data.
#Fay-Herriot 모형 #계층적 베이지안 #로그 변환 #측정오차. #Fay-Herriot model #Hierarchical Bayesian #Log-transformed #Measure ment error.

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