Optimal sample redesign under GREG in skewed populations with application - ARCHIVED
Articles and reports: 12-001-X19960022986
Within a survey re-engineering context, the combined methodology developed in the paper addresses the problem of finding the minimal sample size for the generalized regression estimator in skewed survey populations (e.g., business, institutional, agriculture populations). Three components necessary in identifying an efficient sample redesign strategy involve i) constructing an efficient partitioning between the “take-all” and “sampled” groups, ii) identifying an efficient sample selection scheme, and iii) finding the minimal sample size required to meet the desired precision constraint(s). A scheme named the “Transfer Algorithm” is devised to address the first issue (Pandher 1995) and is integrated with the other two components to arrive at a combined iterative procedure that converges to a globally minimal sample size and population partitioning under the imposed precision constraint. An equivalence result is obtained allowing the solution to the proposed algorithm to be alternatively determined in terms of simple quantities computable directly from the population auxiliary data. Results from the application of the proposed sample redesign methodology to the Local Government Survey in Ontario are reported. A 52% reduction in the total sample size is achieved for the regression estimator of the total at a minimum coefficient of variation of 2%.
Main Product: Survey Methodology
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December 16, 1996 |
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