GLS Estimation and Empirical Bayes Prediction for Linear Mixed Models with Heteroskedasticity and Sampling Weights : A Background Study for the POVMAP Project

van der Weide, Roy

This note adapts results by Huang and Hidiroglou (2003) on Generalized Least Squares estimation and Empirical Bayes prediction for linear mixed models with sampling weights. The objective is to incorporate these results into the poverty mapping approach put forward by Elbers et al. (2003). The estimators presented here have been implemented in version 2.5 of POVMAP, the custom-made poverty mapping software developed by the World Bank

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Year of publication:	2014
Authors:	Weide, Roy van der
Other Persons:	Weide, Roy van der (contributor)
Publisher:	Washington, D.C : The World Bank
Subject:	Kleinste-Quadrate-Methode \| Least squares method \| Stichprobenerhebung \| Sampling \| Bayes-Statistik \| Bayesian inference \| Mathematische Optimierung \| Mathematical programming \| Empirische Methode \| Empirical method \| Theorie \| Theory

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van der Weide, Roy:

GLS Estimation and Empirical Bayes Prediction for Linear Mixed Models with Heteroskedasticity and Sampling Weights

Persistent link: https://www.econbiz.de/10011396309