On calibrated weights in stratified sampling


  • Dinesh Krishna Rao The University of the South Pacific
  • M Khan The University of the South Pacific
  • G Singh The University of the South Pacific




Stratified sampling, calibration estimation, auxiliary information


In this paper, we propose a calibration estimator of population mean in stratified sampling using the known mean and variance information from multi-auxiliary variables. The problem of determining the optimum calibrated weights is formulated as a Nonlinear Programming Problem (NLPP) that is solved using the Lagrange multiplier technique. Numerical example with real data is presented to illustrate the computational details of the proposed estimator. A comparison study is also carried out using real and simulated data to evaluate the performance and the usefulness of the proposed estimator. The study reveals that the proposed estimator with multi-auxiliary information is more efficient estimator of the population mean as it provides least estimated variance and highest gain in relative efficiency (RE). References
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Author Biography

Dinesh Krishna Rao, The University of the South Pacific

Lecturer in Mathematics/Statistics





Proceedings Engineering Mathematics and Applications Conference