Estimation of finite population distribution function with auxiliary information in a complex survey sampling
In this paper, we consider the problem of estimating the finite population cumulative distribution function (CDF) in a complex survey sampling, which includes two-stage and three-stage cluster sampling schemes with and without stratification. We propose two new families of CDF estimators using suppl...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:264486 |
| Acceso en línea: | https://ddd.uab.cat/record/264486 https://dx.doi.org/urn:doi:10.2436/20.8080.02.118 |
| Access Level: | acceso abierto |
| Palabra clave: | Ratio estimator Exponential ratio estimator Auxiliary information Stratification Two-stage and three-stage cluster sampling Relative efficiencies Bias Mean-squared error |
| Sumario: | In this paper, we consider the problem of estimating the finite population cumulative distribution function (CDF) in a complex survey sampling, which includes two-stage and three-stage cluster sampling schemes with and without stratification. We propose two new families of CDF estimators using supplementary information on a single auxiliary variable. Explicit mathematical expressions of the biases and mean squared errors of the proposed CDF estimators are developed under the frst order of the approximation. Real datasets are also considered to support the proposed theory. |
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