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Abstract Details
Activity Number:
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625
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #305093 |
Title:
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New Model-Optimized Sampling Techniques
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Author(s):
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Sung Joon Hong*+ and Sun Woong Kim
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Companies:
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Dongguk University and Dongguk University
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Address:
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Pildong, 3 Ga 26 Jung Gu, Seoul 100-715, , South Korea
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Keywords:
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superpopulation ;
general polynomial model ;
model optimization
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Abstract:
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The work of Kim, Heeringa, and Solenberger (2006) provided a theoretical basis of what is called model-optimized sampling methods for yielding sampling designs that give large variance reductions as well as the stability of the variance estimates. Their methods were based on a simple linear regression superpopulation model. Hong et al. (2009) suggested modified sampling methods based on the same model. However, in many real populations, using more complicated superpopulation models would be better with respect to the efficiency. For this, we suggest model-optimized sampling methods using general polynomial superpopulation models. We illustrate the benefits of our new approaches by comparing the efficiencies between the different models.
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Authors who are presenting talks have a * after their name.
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