On Comparative Analysis of Optimum Allocation Procedures in a Multivariate Stratified Sampling (Review Completed - Accepted)
In multivariate sampling, the major interest is on the problem of estimation of several population characteristics which often make conflicting demands on the sampling procedure. In this type of survey, the best allocation for one item may not in general be the best for another. There is the need to come up with compromise solution in a survey with many characteristics under study. This paper focuses on comparing some techniques of optimum sample allocation which are Yates/Chatterjee, Booth and Sedransk and Vector Maximum Criterion (VMC) on five sets of real life data stratified into six strata and two variates with desired variances using: (i.) Method of maximum variances with fixed n and (ii.) arbitrary fixing of variances. The stratum sample size nh among the classes are obtained to ascertain the criterion that will produce the smallest n. Based on the set of data collected and used for the empirical study it was discovered that Vector Maximum Criterion (VMC), Booth and Sedransk are superior to Yates/Chatterjee
Keywords: Compromise Solution, Iterative Solution, Loss Function Solution, Multivariate Stratified Sampling, Optimum Allocation, Strata, Stratum Weight, True Variance, Variate, Vector Maximum Criterion