Some Methods of Model-Based Sampling

• Activity Number: 99
• Type: Topic Contributed
• Date/Time: Monday, August 3, 2009 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Sung-Joon Hong*+ and So-Hyung Park and Sun-Woong Kim and Hong-Yup Ahn and Steven G. Heeringa
• Companies: Dongguk University and Dongguk University and Dongguk University and Dongguk University and University of Michigan
• Keywords: regression superpopulation model ; average variance ; optimization ; Harvey's algorithm ; restricted maximum likelihood

Abstract

Since Hansen and Hurwitz (1943), a variety of sampling techniques with unequal probabilities have been developed, but the variance of estimates of interest may be quite sensitive to the procedures of selection, especially in the case of small populations. Kim, Heeringa, and Solenberger (2006, 2008) developed model-based sampling methods. Their approaches are based on a fairly practical linear superpopulation model and optimization theory, and may consistently yield optimal sampling designs reducing the variance of the Horvitz and Thompson (1952)'s estimator. In this paper, we suggest new model-based sampling methods and empirically compare them with the previous methods and the traditional sampling methods of Brewer (1963) and Murthy (1957). Also, we compare the efficiencies of those alternative methods according to some chosen estimation methods of model parameters.