Estimation and Prediction in Transformed Nested Error Regression Models

Abstract

This paper suggests parametrically transformed nested error regression models (TNERM), which transform the data flexibly to follow the normal linear mixed regression. We provide a procedure for estimating consistently the parameters of the proposed model and a predictor based on the consistent estimators. Then, in order to calibrate uncertainty of the transformed empirical best linear unbiased predictor, we derive prediction intervals with second-order accuracy based on the parametric bootstrap method. The proposed methods are investigated through simulation and empirical studies.

This manuscript is superseded by "Adaptively transformed mixed model prediction of general finite population parameters" by Sugasawa and Kubokawa (arXiv:1705.04136)

References in corpus (3)

• New Important Developments in Small Area Estimation
• Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models
• Parametric Transformed Fay-Herriot Model for Small Area Estimation