Khan, Mohammad G.M. and Rashid, Mahmood A. and Sharma, Sushita (2019) An optimal multivariate cluster sampling design. Communications in Statistics: Theory and Methods, TBC . pp. 1-8. ISSN 0361-0926
Full text not available from this repository. (Request a copy)Abstract
AbstractThis article presents a problem of determining optimum cluster size and sampling units in multivariate surveys. When a cluster sampling design is to be used and more than one characteristic is under study, it is usually not advisable to use the individual optimum cluster size and sampling unit for one reason or the other. In such situations, some criterion is needed to work out an acceptable cluster size and sampling unit which are optimum for all characteristics in some sense. Moreover, for practical implementation of sample size, we need integer values of the cluster size and sampling unit. Therefore, the present article addresses the problem of determining integer optimum compromise cluster size and sampling unit when the population means of the various characteristics are of interest. Formulating the problem as an All Integer Nonlinear Programing Problem (AINLPP), the article develops a solution procedure using a Genetic algorithm. The compromise solution discussed is the optimal in the sense that it minimizes a weighted sum of the variances of the cluster sample means of various characteristics under study. Two numerical examples illustrate the practical application of the solution procedure. The results show that the proposed technique can be efficiently applied in determining the sample size in multivariate cluster sampling design.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 26 Jun 2019 02:42 |
| Last Modified: | 26 Jun 2019 02:42 |
| URI: | https://repository.usp.ac.fj/id/eprint/11624 |
Actions (login required)
 |
View Item |