On Combining Ratio and Product Type Estimators For Estimation of Finite Population Mean In Adaptive Cluster Sampling Design
This article introduces a novel class of estimators and several new novel member estimators, combining the ratio and product forms, within the framework of Adaptive Cluster Sampling (ACS) design for estimating finite population mean. Specifically designed for rare or hidden clustered populations, th...
| Authors: | , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2024 |
| Country: | Brasil |
| Institution: | Universidade Federal de Lavras (UFLA) |
| Repository: | Brazilian Journal of Biometrics |
| Language: | English |
| OAI Identifier: | oai:biometria.ufla.br:article/725 |
| Online Access: | https://biometria.ufla.br/index.php/BBJ/article/view/725 |
| Access Level: | Open access |
| Keyword: | Adaptive cluster sampling Sampling Adaptive design Ratio-cum-product Class of estimators Adaptative cluster sampling |
| Summary: | This article introduces a novel class of estimators and several new novel member estimators, combining the ratio and product forms, within the framework of Adaptive Cluster Sampling (ACS) design for estimating finite population mean. Specifically designed for rare or hidden clustered populations, the new novel estimators developed from the proposed class offer enhanced efficiency in estimation. To study the proposed class comprehensively, we derive expressions for the bias and Mean Squared Error (MSE) up to the first order of approximation. Through comprehensive simulation studies, we demonstrate the superior efficiency of the new developed estimators over several existing alternatives considered in this study. |
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