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Integer compromise allocation in multivariate stratified surveys

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Listed:
  • Rahul Varshney
  • M. Khan
  • Ummatul Fatima
  • M. Ahsan

Abstract

In multivariate stratified sampling more than one characteristic are defined on every unit of the population. An optimum allocation which is optimum for one characteristic will generally be far from optimum for others. To resolve this problem, a compromise criterion is needed to work out a usable allocation. In this manuscript, a compromise criterion is discussed and integer compromise allocations are obtained by using goal programming technique. A numerical example is presented to illustrate the computational details, which reveals that the proposed criterion is suitable for working out a usable compromise allocation for multivariate stratified surveys. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Rahul Varshney & M. Khan & Ummatul Fatima & M. Ahsan, 2015. "Integer compromise allocation in multivariate stratified surveys," Annals of Operations Research, Springer, vol. 226(1), pages 659-668, March.
    • Handle: RePEc:spr:annopr:v:226:y:2015:i:1:p:659-668:10.1007/s10479-014-1734-z
    DOI: 10.1007/s10479-014-1734-z
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    References listed on IDEAS

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    1. M. Ahsan & S. Khan, 1982. "Optimum allocation in multivariate stratified random sampling with overhead cost," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 29(1), pages 71-78, December.
    2. M. G. M. Khan & M. J. Ahsan & Nujhat Jahan, 1997. "Compromise allocation in multivariate stratified sampling: An integer solution," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(1), pages 69-79, February.
    3. M.G.M. Khan & E.A. Khan & M.J. Ahsan, 2003. "Theory & Methods: An Optimal Multivariate Stratified Sampling Design Using Dynamic Programming," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 45(1), pages 107-113, March.
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