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Virginia Polytechnic Institute and State University

Design and regression estimation in double sampling

Abstract

dc:description.abstract

Two methods developed to improve regression estimation in double sampling under the superpopulation model approach are examined. One method proposes the use of an alternative double sample regression estimator. The other method recommends the use of nonrandom, purposive subsampling plans. Both methods aim to reduce the mean squared errors of regression estimators in double sampling. A major criticism against the superpopulation model approach is its strong dependence on the correctness of the assumed model. Thus, two purposive subsampling plans were considered. The first plan designed subsamples based on the assumption that the superpopulation model was a first order linear model. The second plan selected subsamples that guarded against the occurrence of a second order model. As expected, the designed subsamples without protection can be very sensitive to the presence of a second order linear model. On the other hand, the designed subsamples with protection rendered the double sample regression estimators robust not only to a second order superpopulation model but also fairly robust to other slight model deviations such as variance misspecification. Therefore the use of designed subsamples with protection against a second order model is suggested whenever a first order superpopulation model is uncertain. Under designed subsamples with or without protection, the alternative double sample regression estimator is not found to be more efficient than the usual double sample regression estimator found in most sampling textbooks . However, the alternative double sample regression estimator has shown itself to be more efficient under simple random subsampling when the correlation between variables is weak and subsamples are small.

Degree

thesis:*
Name thesis:degree_name
Ph. D.
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Statistics
Department dc:contributor.department
Statistics
Grantor dc:publisher
Virginia Polytechnic Institute and State University
Year dc:date.issued
1987

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Tan, Edith Estillore
Chair dc:contributor.committeechair
  • Arnold, Jesse C.
Committee members dc:contributor.committeemember
  • Myers, Raymond
  • Foutz, Robert V.
  • Brooks, Camilla A.
  • Hinkelmann, Klaus

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en_US

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/10919/74777
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/74777

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Tan, Edith Estillore. Design and regression estimation in double sampling. doctoral thesis, Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/74777