Title: Fay-Herriot model with functional measurement error of outcome variable and covariate
Authors: Jinseub Hwang - Daegu University (Korea, South) [presenting]
Chaeyeong Kang - Daegu university (Korea, South)
Do Hyang Kim - Daegu University (Korea, South)
Soo Rack Ryu - Daegu University (Korea, South)
Abstract: Various extension versions for Fay-Herriot model have been proposed. However, most studies have considered only covariate measurement errors, and the outcome variable may also have measurement errors. We propose an extended Fay-Herriot model that can reflect the measurement error of dependent variable and covariate. We consider a functional measurement error model that assumes a non-stochastic true value of the outcome variable and covariate. To fit the model and estimate parameters, we consider hierarchical Bayesian model approach based on Markov chain Monte Carlo method. To check the superiority of the proposed model, we two linear functions in simulation studies, and we use the seventh KNHANES (Korean national health and nutrition examination survey) data and 2010 Italian household budget survey in the application. As a result for simulation studies and application, the performance of the proposed model is better based.