Abstract: Calibration estimation is a method of adjusting the
original design weights to improve the survey estimates by using
auxiliary information such as the known population total (or mean)
of the auxiliary variables. A calibration estimator uses calibrated
weights that are determined to minimize a given distance measure to
the original design weights while satisfying a set of constraints
related to the auxiliary information. In this paper, we propose a new
multivariate calibration estimator for the population mean in the
stratified sampling design, which incorporates information available
for more than one auxiliary variable. The problem of determining the
optimum calibrated weights is formulated as a Mathematical
Programming Problem (MPP) that is solved using the Lagrange
multiplier technique.