A GENERAL CLASS OF MULTI-PHASE MULTIVARIATE MIXTURE RATIO ESTIMATORS IN THE PARTIAL INFORMATION CASE

In this article, we proposed three classes of multiphase multivariate mixture ratio estimators for estimating the population mean with multi-auxiliary characteristics in partial information case (PIC) situation. The suggested estimators (PIC-I, PIC-II and PIC-III) combine data from many auxiliary variables and auxiliary attributes to reduce the mean square error (MSE) to go beyond the existing single-variable approach. The analytical expressions for the MSE of the estimators are derived up to the first order of approximation. The efficiency of the proposed estimators is theoretically compared with the existing ratio-type partial information case estimator. Empirical results based on simulated data demonstrated that the proposed multivariate mixture estimators in partial information case situations (PIC-I, PIC-II and PIC-III) were consistently more efficient than the existing partial information case estimator.