Abstract/Details

Use of random permutation model in rate estimation and standardization


2003 2003

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Abstract (summary)

Through integrating techniques from several areas in survey sampling, we develop an alternative method of deriving estimators using random permutation models under (stratified) simple random sampling without replacement. The finite random permutation model links the samples to the population. The joint permutation of response and auxiliary variables is modeled using seemingly unrelated regression. We use prediction theory from the super-population sampling literature to derive the linear unbiased minimum variance predictors of population means under the design-based framework using the finite estimating equation approach of Binder and Patak (1994). The predictors have functional forms similar to those derived using design-based, model-assisted and calibration approaches, but depend on neither superpopulation nor regression model assumptions. We applied the results to standardization of multiple rates, and illustrate how our methods account for the covariance of the standardized rates, unlike conventional standardization methods.

Indexing (details)


Subject
Biostatistics;
Public health;
Models;
Standardization
Classification
0308: Biostatistics
0573: Public health
Identifier / keyword
Health and environmental sciences, Biological sciences, Finite sampling, Random permutation, Rate estimation, Standardization
Title
Use of random permutation model in rate estimation and standardization
Author
Li, Wenjun
Number of pages
180
Publication year
2003
Degree date
2003
School code
0118
Source
DAI-B 64/01, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780493997162, 0493997164
Advisor
Stanek, Edward J., III
University/institution
University of Massachusetts Amherst
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3078702
ProQuest document ID
305327010
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305327010
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