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Integer compromise allocation in multivariate stratified surveys

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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.

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References

  • Ahsan, M. J. (1975–76). A procedure for the problem of optimum allocation in multivariate stratified random sampling. Aligarh Bulletin of Mathematics, 5–6, 37–42.

  • Ahsan, M. J. (1978). Allocation problem in multivariate stratified random sampling. Journal of Indian Statistical Association, 16, 1–5.

    Google Scholar 

  • Ahsan, M. J., & Khan, S. U. (1977). Optimum allocation in multivariate stratified random sampling using prior information. Journal of Indian Statistical Association, 15, 57–67.

    Google Scholar 

  • Ahsan, M. J., & Khan, S. U. (1982). Optimum allocation in multivariate stratified random sampling with overhead cost. Metrika, 29, 71–78.

    Article  Google Scholar 

  • Ansari, A. H., Najmussehar, & Ahsan, M. J. (2009). On multiple response stratified random sampling design. Journal of Statistics Sciences, 1(1), 45–54.

  • Ansari, A. H., Varshney, R., Najmussehar, & Ahsan, M. J. (2011). An optimum multivariate-multiobjective stratified sampling design. Metron, LXIX(3), 227–250.

    Article  Google Scholar 

  • Aoyama, H. (1962). Stratified random sampling with optimum allocation for multivariate population. Annals of the Institute of Statistical Mathematics, 14, 251–258.

    Article  Google Scholar 

  • Arthanari, T. S., & Dodge, Y. (1981). Mathematical Programming in Statistics. New York: Wiley.

    Google Scholar 

  • Bethel, J. (1985). An optimum allocation algorithm for multivariate surveys. Proceedings of American Statistical Association Survey Research Methods Section, 209–212.

  • Bethel, J. (1989). Sample allocation in multivariate surveys. Survey Methodology, 15, 47–57.

    Google Scholar 

  • Chatterjee, S. (1967). A note on optimum allocation. Scandinavian Actuarial Journal, 50, 40–44.

    Article  Google Scholar 

  • Chatterjee, S. (1968). Multivariate stratified surveys. Journal of the American Statistical Association, 63, 530–534.

    Google Scholar 

  • Chromy, J. R. (1987). Design optimization with multiple objectives. Proceedings of American Statistical Association Survey Research Methods Section, 194–199.

  • Cochran, W. G. (1977). Sampling techniques. New York: Wiley.

    Google Scholar 

  • Dalenius, T. (1957). Sampling in Sweden: Contributions to the methods and theories of sample survey practice. Stockholm: Almqvist and Wiksell.

    Google Scholar 

  • Díaz-García, J. A., & Cortez, L. U. (2006). Optimum allocation in multivariate stratified sampling: Multi-objective programming. Comunicación Técnica No. I-06-07/28-03-2006 (PE/CIMAT).

  • Díaz-García, J. A., & Cortez, L. U. (2008). Multi-objective optimisation for optimum allocation in multivariate stratified sampling. Survey Methodology, 34(2), 215–222.

    Google Scholar 

  • Folks, J. L., & Antle, C. E. (1965). Optimum allocation of sampling units to strata when there are R responses of interest. Journal of the American Statistical Association, 60, 225–233.

    Google Scholar 

  • Ghosh, S. P. (1958). A note on stratified random sampling with multiple characters. Calcutta Statistical Association Bulletin, 8, 81–89.

    Google Scholar 

  • Jahan, N., & Ahsan, M. J. (1995). Optimum allocation using separable programming. Dhaka University Journal of Sciences, 43(1), 157–164.

    Google Scholar 

  • Jahan, N., Khan, M. G. M., & Ahsan, M. J. (1994). A generalized compromise allocation. Journal of the Indian Statistical Association, 32, 95–101.

    Google Scholar 

  • Jahan, N., Khan, M. G. M., & Ahsan, M. J. (2001). Optimum compromise allocation using dynamic programming. Dhaka University Journal of Sciences, 49(2), 197–202.

    Google Scholar 

  • Khan, M. G. M., Ahsan, M. J., & Jahan, N. (1997). Compromise allocation in multivariate stratified sampling: An integer solution. Naval Research Logistics, 44, 69–79.

    Article  Google Scholar 

  • Khan, M. G. M., Khan, E. A., & Ahsan, M. J. (2003). An optimal multivariate stratified sampling design using dynamic programming. Australian & New Zealand Journal of Statistics, 45(1), 107–113.

    Article  Google Scholar 

  • Khan, M. G. M., Khan, E. A., & Ahsan, M. J. (2008). Optimum allocation in multivariate stratified sampling in presence of non-response. Journal of Indian Society of Agricultural Statistics, 62(1), 42–48.

    Google Scholar 

  • Khan, M. G. M., Maiti, T., & Ahsan, M. J. (2010). An optimal multivariate stratified sampling design using auxiliary information: An integer solution using goal programming approach. Journal of Official Statistics, 26(4), 695–708.

    Google Scholar 

  • Kokan, A. R., & Khan, S. U. (1967). Optimum allocation in multivariate surveys: An analytical solution. Journal of Royal Statistical Society, B29(1), 115–125.

    Google Scholar 

  • Kozak, M. (2006a). On sample allocation in multivariate surveys. Communications in Statistics Simulation and Computation, 35, 901–910.

    Article  Google Scholar 

  • Kozak, M. (2006b). Multivariate sample allocation: Application of random search method. Statistics in Transition, 7(4), 889–900.

    Google Scholar 

  • Kreienbrock, L. (1993). Generalized measures of dispersion to solve the allocation problem in multivariate stratified random sampling. Communications in Statistics Theory and Methods, 22(1), 219–239.

    Article  Google Scholar 

  • LINGO (2001). LINGO-User’s Guide. Published by LINDO SYSTEM INC., 1415, North Dayton Street, Chicago, Illinois, 60622, USA.

  • Neyman, J. (1934). On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society, 97(4), 558–625.

    Article  Google Scholar 

  • Rahim, M. A. (1995). Use of distance function to optimize sample allocation in multivariate surveys: A new perspective. Proceedings of American Statistical Association Survey Research Methods Section, 365–369.

  • Schittkowski, K. (1985-86). NLPQL: A FORTRAN subroutine solving constrained nonlinear programming problems. Annals of Operations Research, 5, 485–500.

  • Schniederjans, M. J. (1995). Goal programming: Methodology and applications. Dordrecht: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Semiz, M. (2004). Determination of compromise integer strata sample sizes using goal programming. Hacettepe Journal of Mathematics and Statistics, 33, 91–96.

  • Singh, S. (2003). Advanced sampling theory with applications: How Michael ‘selected’ Amy. Dordrecht: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Stuart, A. (1954). A simple presentation of optimum sampling results. Journal of the Royal Statistical Society, B16(2), 239–241.

    Google Scholar 

  • Sukhatme, P. V., Sukhatme, B. V., Sukhatme, S., & Asok, C. (1984). Sampling theory of surveys with applications. Iowa State University Press, Iowa and Indian Society of Agricultural Statistics, New Delhi.

  • Tschuprow, A. A. (1923). On the mathematical expectation of the moments of frequency distributions in the case of correlated observations. Metron, 2, 461–493.

    Google Scholar 

  • Varshney, R., Najmussehar, & Ahsan, M. J. (2012). An optimum multivariate stratified double sampling design in presence of non-response. Optimization Letters, 6(5), 993–1008.

    Article  Google Scholar 

  • Yates, F. (1960). Sampling methods for censuses and surveys. London: Charles Griffin and Co., Ltd.

    Google Scholar 

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Acknowledgments

The authors are grateful to the Editors and the learned Reviewers for their valuable comments and suggestions that helped to revise the manuscript in its present form. The author M. J. Ahsan is thankful for the financial assistance provided under ‘Emeritus Fellowship’ by the University Grants Commission, Govt. of India to carry out this research.

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Correspondence to Rahul Varshney.

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Varshney, R., Khan, M.G.M., Fatima, U. et al. Integer compromise allocation in multivariate stratified surveys. Ann Oper Res 226, 659–668 (2015). https://doi.org/10.1007/s10479-014-1734-z

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