Improving both domain and total area estimation by composition
In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such a design, one fraction of the sample is distr...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2004 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3741 |
| Acceso en línea: | https://hdl.handle.net/2099/3741 |
| Access Level: | acceso abierto |
| Palabra clave: | Inference Multivariate analysis Inferència Anàlisi multivariable Classificació AMS::62 Statistics::62J Linear inference, regression Classificació AMS::62 Statistics::62H Multivariate analysis |
| Sumario: | In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such a design, one fraction of the sample is distributed proportionally among the small areas and the rest is evenly distributed. Simulation is used to assess the performance of the direct estimator and two composite small area estimators, for a range of sample sizes and different sample distributions. Performance is measured in terms of mean squared errors for both small and large area parameters. Small area composite estimators open the possibility of reducing the sample size when the desired precision is given, or improving precision for a given sample size. |
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