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Mathematical programming on multivariate calibration estimation in stratified sampling

Rao, Dinesh K. and Khan, Mohammad G.M. and Khan, Sabiha (2012) Mathematical programming on multivariate calibration estimation in stratified sampling. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 6 (12). pp. 58-62. ISSN 2070-3740

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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.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Ms Shalni Sanjana
Date Deposited: 29 Mar 2013 02:01
Last Modified: 15 Mar 2017 21:58

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