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Abstract

In stratified sampling design when the cost of measuring the units is not significant in each stratum, the estimation of population mean or total constructed from a selected sample according to Neyman allocation is advisable. In general the practical use of Neyman allocation suffers from a number of limitations, when there is no information about strata standard deviations except about the equality of standard deviations between some of the strata, then the precision of the estimate may be increased by pooling the strata with equal standard deviations as a single stratum and the problem of allocation is resolved by using Neyman and proportional allocations simultaneously. In this paper the case of multiple pooling of the standard deviations of the estimates in a multivariate stratified sampling for more than three strata. The problem is formulated as a Multiobjective Nonlinear Programming Problem and its solution procedure is suggested by using Fuzzy Programming approach.

Keywords

Multivariate Stratified Sampling Compromise Allocation Pooled Standard Deviations

Article Details

Author Biographies

Rahul Varshney, Department of Applied Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India

Dr. Rahul Varshney Assistant Professor Department of Applied Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow-226025, India

Srikant Gupta, Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh-202002, India.

Srikant Gupta Research Scholar Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh-202002, India.

Irfan Ali, Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh-202002, India.

Dr. Irfan Ali Assistant Professor Srikant Gupta Research Scholar Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh-202002, India.
How to Cite
Varshney, R., Gupta, S., & Ali, I. (2017). An Optimum Multivariate-Multiobjective Stratified Sampling Design: Fuzzy Programming Approach. Pakistan Journal of Statistics and Operation Research, 13(4), 829-855. https://doi.org/10.18187/pjsor.v13i4.1834