Use of statistical matching techniques in calibration estimation

This article deals with an attempt to cross-tabulate two categorical variables, which were separately collected from two large independent samples, and jointly collected from one small sample. It was assumed that the large samples have a large set of common variables. The proposed estimation technique can be considered a mix between calibration techniques and statistical matching. Through calibration techniques, it is possible to incorporate the complex designs of the samples in the estimation procedure, to fulfill some consistency requirements between estimates from various sources, and to obtain fairly unbiased estimates for the two-way table. Through the statistical matching techniques, it is possible to incorporate a relatively large set of common variables in the calibration estimation, by means of which the precision of the estimated two-way table can be improved. The estimation technique enables us to gain insight into the bias generally obtained, in estimating the two-way table, by sole use of the large samples. It is shown how the estimation technique can be useful to impute values of the one large sample (donor source) into the other large sample (host source). Although the technique is principally developed for catagorical variables Y and Z, with a minor modification, it is also applicable for continuous variables Y and Z.