On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique
In a multivariate stratified sampling design, the individual optimum allocation of one character may not remain optimum to other characteristics. For the solution of such problems, a usable allocation must be required to get precise estimates of the unknown population parameters, which may be near o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-07-01
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| Series: | Journal of King Saud University: Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018364721001099 |
| _version_ | 1819107689217654784 |
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| author | Abdullah Ali H. Ahmadini Rahul Varshney Mradula Irfan Ali |
| author_facet | Abdullah Ali H. Ahmadini Rahul Varshney Mradula Irfan Ali |
| author_sort | Abdullah Ali H. Ahmadini |
| collection | DOAJ |
| description | In a multivariate stratified sampling design, the individual optimum allocation of one character may not remain optimum to other characteristics. For the solution of such problems, a usable allocation must be required to get precise estimates of the unknown population parameters, which may be near optimum to all characteristics in some sense. The compromise criterion is required to obtain such usable allocation in sampling literature. In this paper, the sample allocation problem is considered as a stochastic nonlinear programming problem and thereafter formulated into a multiobjective programming problem to provide the usable allocation. The formulated problem is solved by using different models of stochastic optimization. Afterwards, the proposed allocation is worked out and compared with some other allocations, which are well defined in sampling, to give a comparative study. Also, the numerical study defines the practical utility of the proposed technique. |
| first_indexed | 2024-12-22T02:58:01Z |
| format | Article |
| id | doaj.art-96d9089c411140cdbe50eb77f4c86f73 |
| institution | Directory Open Access Journal |
| issn | 1018-3647 |
| language | English |
| last_indexed | 2024-12-22T02:58:01Z |
| publishDate | 2021-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Science |
| spelling | doaj.art-96d9089c411140cdbe50eb77f4c86f732022-12-21T18:41:14ZengElsevierJournal of King Saud University: Science1018-36472021-07-01335101448On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming techniqueAbdullah Ali H. Ahmadini0Rahul Varshney1 Mradula2Irfan Ali3Department of Mathematics, Faculty of Science, Jazan University, Jazan, Saudi ArabiaDepartment of Statistics, School of Physical & Decision Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow 226 025, IndiaDepartment of Statistics, School of Physical & Decision Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow 226 025, IndiaDepartment of Statistics & Operations Research, Aligarh Muslim University, Aligarh 202 002, India; Corresponding author.In a multivariate stratified sampling design, the individual optimum allocation of one character may not remain optimum to other characteristics. For the solution of such problems, a usable allocation must be required to get precise estimates of the unknown population parameters, which may be near optimum to all characteristics in some sense. The compromise criterion is required to obtain such usable allocation in sampling literature. In this paper, the sample allocation problem is considered as a stochastic nonlinear programming problem and thereafter formulated into a multiobjective programming problem to provide the usable allocation. The formulated problem is solved by using different models of stochastic optimization. Afterwards, the proposed allocation is worked out and compared with some other allocations, which are well defined in sampling, to give a comparative study. Also, the numerical study defines the practical utility of the proposed technique.http://www.sciencedirect.com/science/article/pii/S1018364721001099Multivariate-multiobjective stratified samplingStochastic programmingFuzzy goal programmingCompromise allocationGamma cost function |
| spellingShingle | Abdullah Ali H. Ahmadini Rahul Varshney Mradula Irfan Ali On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique Journal of King Saud University: Science Multivariate-multiobjective stratified sampling Stochastic programming Fuzzy goal programming Compromise allocation Gamma cost function |
| title | On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique |
| title_full | On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique |
| title_fullStr | On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique |
| title_full_unstemmed | On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique |
| title_short | On multivariate-multiobjective stratified sampling design under probabilistic environment: A fuzzy programming technique |
| title_sort | on multivariate multiobjective stratified sampling design under probabilistic environment a fuzzy programming technique |
| topic | Multivariate-multiobjective stratified sampling Stochastic programming Fuzzy goal programming Compromise allocation Gamma cost function |
| url | http://www.sciencedirect.com/science/article/pii/S1018364721001099 |
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