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Abstract Details
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Activity Number:
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554
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Government Statistics
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| Abstract - #300288 |
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Title:
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Decision-Based Estimation for Government Statistics
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Author(s):
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Jun Shao*+ and Sheng Wang
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Companies:
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University of Wisconsin and University of Wisconsin
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Address:
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1300 University Ave, Madison, WI, 53706, USA
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Keywords:
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regression estimators ;
stratified sampling ;
asymptotic distribution ;
variance estimation
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Abstract:
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In the Annual Survey of Public Employment and Payroll and the Annual Finance Survey, a new two-stage stratified sampling design is used to reduce cost and improve data quality. The new sampling design divides each stratum into two sub-strata according to unit size and reduces the sample size of the sub-stratum with small size units. To estimate the population total, a decision-based regression estimation method was proposed in Cheng, Slud and Hogue (2010), which applies hypothesis testing to decide whether a common regression should be applied in two sub-strata. In this presentation we discuss asymptotic properties of the decision-based method as well as variance estimation for decision based regression estimators. Some empirical results are also presented.
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