Multivariate Multi-Objective Allocation in Stratified Random Sampling: A Game Theoretic Approach

We consider the problem of multivariate multi-objective allocation where no or limited information is available within the stratum variance. Results show that a game theoretic approach (based on weighted goal programming) can be applied to sample size allocation problems. We use simulation technique...

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Bibliographic Details
Published inPloS one Vol. 11; no. 12; p. e0167705
Main Authors Muhammad, Yousaf Shad, Hussain, Ijaz, Shoukry, Alaa Mohamd
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 09.12.2016
Public Library of Science (PLoS)
Subjects
Algorithms
Animals
Applied mathematics
Biology and Life Sciences
Cooperation
Design optimization
Economic models
Game Theory
Games
Goal programming
Humans
Minimax technique
Multiple objective analysis
Multivariate Analysis
Optimization techniques
People and Places
Physical Sciences
Population
Population Density
Random Allocation
Random sampling
Reputations
Research and Analysis Methods
Sample Size
Simulation
Statistical sampling
Stratigraphy
Islamabad Pakistan
Pakistan
Kolkata India
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Summary:We consider the problem of multivariate multi-objective allocation where no or limited information is available within the stratum variance. Results show that a game theoretic approach (based on weighted goal programming) can be applied to sample size allocation problems. We use simulation technique to determine payoff matrix and to solve a minimax game.
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Conceptualization: YSM.Data curation: YSM.Formal analysis: YSM.Funding acquisition: YSM.Investigation: YSM.Methodology: YSM.Project administration: AMS.Resources: AMS.Software: IH.Supervision: YSM.Validation: IH.Visualization: IH AMS.Writing – original draft: YSM.Writing – review & editing: IH AMS.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0167705