An empirical evaluation of five small area estimators
This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated we...
| Authors: | , , |
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| Format: | article |
| Publication Date: | 2003 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3728 |
| Online Access: | https://hdl.handle.net/2099/3728 |
| Access Level: | Open access |
| Keyword: | Inference Multivariate analysis Inferència Anàlisi multivariable Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62H Multivariate analysis |
| Summary: | This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found that among the feasible estimators, the best choice is the one that uses area-specific estimates of variance and squared bias. |
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