Estimation of Finite Population Mean in Multivariate Stratified Sampling under Cost Function Using Goal Programming

الملخص EN

In practical utilization of stratified random sampling scheme, the investigator meets a problem to select a sample that maximizes the precision of a finite population mean under cost constraint.

An allocation of sample size becomes complicated when more than one characteristic is observed from each selected unit in a sample.

In many real life situations, a linear cost function of a sample size nh is not a good approximation to actual cost of sample survey when traveling cost between selected units in a stratum is significant.

In this paper, sample allocation problem in multivariate stratified random sampling with proposed cost function is formulated in integer nonlinear multiobjective mathematical programming.

A solution procedure is proposed using extended lexicographic goal programming approach.

A numerical example is presented to illustrate the computational details and to compare the efficiency of proposed compromise allocation.