Abstract #301058

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JSM 2003 Abstract #301058
Activity Number: 170
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #301058
Title: Probability Sampling Scheme for Variance Minimization
Author(s): Sun Woong Kim*+ and Steven G. Heeringa and Peter Solenberger
Companies: Dongguk University and University of Michigan and University of Michigan
Address: Department of Statistics, Seoul, , 100-715, S KOREA
Keywords: statistical efficiency ; inclusion probability ; nonlinear programming
Abstract:

A number of techniques for probability sampling without replacement (SWOR) have been introduced, although it is not clear which method is superior in terms of statistical efficiency. Jessen (1969) suggested one such sampling technique. The Jessen method 4 shows high efficiency in comparisons of variances of estimates relative to those of alternative SWOR selection schemes. However, Jessen's method may be difficult to employ in practical problems due to the arbitrariness and complexities of trials to determine the joint inclusion probabilities that are required for the variance estimation formula suggested by Yates and Grundy (1953). We suggest a nonlinear programming (NLP) approach to overcome some computational disadvantages of Jessen's method 4. We illustrate the practicality and statistical efficiency of our method.

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