GLS Estimation and Empirical Bayes Prediction for Linear Mixed Models with Heteroskedasticity and Sampling Weights
A Background Study for the Povmap Project
Bibliographic Data
| ID | 23536151 |
|---|---|
| Authors | Roy van der Weide (0009-0003-7901-3997, corresponding author) |
| Year | 2014 |
| Publication date | 2014-09-01 |
| Open Access | No |
| Type | BOOK |
| Publisher | World Bank Group, Washington, DC (PUBLISHER) |
| DOI | 10.1596/1813-9450-7028 |
| OpenAlex | W1837889401 |
| Language | EN |
| Citations received | 3 |
| References cited | 3 |
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.
Bayes' theorem · Bayesian probability · Econometrics · Economics · Estimation · Heteroscedasticity · Machine learning · Naive Bayes classifier · Sampling (signal processing) · Statistics · Support vector machine · Agricultural risk and resilience · Computer Science · Economics of Agriculture and Food Markets · Income, Poverty, and Inequality · Mathematics
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2020 - 2025 (6) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |