TY - JFULL
AU - Dinesh Rao and M.G.M. Khan and Sabiha Khan
PY - 2012/1/
TI - Mathematical Programming on Multivariate Calibration Estimation in Stratified Sampling
T2 - International Journal of Mathematical and Computational Sciences
SP - 1622
EP - 1627
VL - 6
SN - 1307-6892
UR - https://publications.waset.org/pdf/10096
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 72, 2012
N2 - 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.
ER -