Abstract/Details

Developing best linear unbiased estimator in finite population accounting for measurement error due to interviewer


2010 2010

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Abstract (summary)

Godambe (1955) give a general finite population sampling model and proved that a best linear unbiased estimator (BLUE) of population total does not exist when there is no measurement error. In this research, Godambe’s linear estimator is expanded to include two types of measurement errors and their mixture. We check Godambe’s non-existence theorem and explore the method to develop the best linear unbiased estimator of the latent population total by using individual unbiased constraints and average unbiased constraints, respectively. We start from Godambe’s general framework and then reduce to two probability models which are less general than Godambe’s. The model is developed under unequal probability sampling without replacement. As a special case, the model under simple random sampling without replacement can be derived directly based on the results. The traditional definition of inclusion probability is extended and applied to the unequal probability sampling. These results connect the traditional sampling method and the design-based method using random permutation models based on the work of Royall (1976) as proposed by Stanek, Singer and Lencina (2004). We also examine the relationship among Godambe’s general finite sampling model, the expanded model finite population model and the finite population mixed model. Also, we are able to give another set of solutions by giving a distribution to the sample latent values. The research can serve as the basis for extensions to multi-stage sampling or other complex sampling designs.

Indexing (details)


Subject
Biostatistics;
Statistics
Classification
0308: Biostatistics
0463: Statistics
Identifier / keyword
Pure sciences; Biological sciences; Interviewer; Measurement errors; Survey sampling
Title
Developing best linear unbiased estimator in finite population accounting for measurement error due to interviewer
Author
Zhang, Ruitao
Number of pages
205
Publication year
2010
Degree date
2010
School code
0118
Source
DAI-B 71/12, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781124321264
Advisor
III, Edward J. Stanek
Committee member
Hsieh, H. K.; Lin, Rongheng
University/institution
University of Massachusetts Amherst
Department
Public Health
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3427614
ProQuest document ID
814880807
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/814880807
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