Compromise allocation problem in multivariate stratified sampling with flexible fuzzy goals

In a multivariate stratified sample survey, we assumed p-characteristics which are to be measured on each unit of the population and the population is further subdivided into L subpopulations. For estimating the p-population means of all characteristics, which are not known in advance usually, a ran...

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Bibliographic Details
Published in:Journal of statistical computation and simulation Vol. 90; no. 9; pp. 1557 - 1569
Main Authors: Haq, Ahteshamul, Ali, Irfan, Varshney, Rahul
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 12.06.2020
Taylor & Francis Ltd
Subjects:
binary goal programming
flexible fuzzy constraint
Goal programming
Hansen-Hurwitz technique
Linear programming
Multi-objective decision-making problem
multiple goal values
Multivariate analysis
Nonlinear programming
Population
Random sampling
ISSN:0094-9655, 1563-5163
Online Access:Get full text
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Summary:In a multivariate stratified sample survey, we assumed p-characteristics which are to be measured on each unit of the population and the population is further subdivided into L subpopulations. For estimating the p-population means of all characteristics, which are not known in advance usually, a random sample is taken out from the population with the help of simple random sampling. In a multivariate stratified sample survey, the optimum allocation of one character is not considered as optimum for others. Then a solution is needed to work out an allocation that may be optimum for all characteristics in some sense, called as compromise allocation in sampling literature. The estimation of p-population means in the presence of non-response, for a fixed cost, is discussed. The formulated integer non-linear programming problem is converted into a binary goal programming problem. The problem's solution is obtained by using the concept of flexible fuzzy goal programming.
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ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2020.1734808