A multivariate regression-cum-exponential estimator for population variance vector in two phase sampling
| Autor: | Aamir Sanaullah, Muhammad Hanif, Amber Asghar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Multivariate statistics
Multidisciplinary Mean squared error Univariate 02 engineering and technology 01 natural sciences 010104 statistics & probability Minimum-variance unbiased estimator Efficient estimator Multivariate analysis of variance Bias of an estimator Statistics 0202 electrical engineering electronic engineering information engineering Econometrics Statistics::Methodology 020201 artificial intelligence & image processing 0101 mathematics General lcsh:Science (General) Mathematics Population variance lcsh:Q1-390 |
| Zdroj: | Journal of King Saud University: Science, Vol 30, Iss 2, Pp 223-228 (2018) |
| ISSN: | 1018-3647 |
| Popis: | In this study we have proposed a multivariate regression-cum-exponential type estimator for estimating a vector of population variance. In the present study, unknown population variance vector estimation has been discussed using multi-auxiliary variables in two-phase sampling and different cases have also been derived. A comparison between existing and the proposed multivariate, bivariate and univariate estimators has been prepared with the help of a real data for estimating population variance. A simulation study for multivariate estimator using multi-auxiliary variables has also been carried out to demonstrate the performance of the estimators. Keywords: Multivariate estimator, Multi-auxiliary variables, Two-phase sampling, Regression estimator, Variance-covariance matrix, Simulation |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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