Document Detail

Ranked set sampling for efficient estimation of a population proportion.
MedLine Citation:
PMID:  16100735     Owner:  NLM     Status:  MEDLINE    
Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a population proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a population proportion.
Haiying Chen; Elizabeth A Stasny; Douglas A Wolfe
Related Documents :
21180845 - Spatial distribution of mortality by homicide and social inequalities according to race...
3180745 - Estimation of probabilities using the logistic model in retrospective studies.
15639705 - Free knot splines for logistic models and threshold selection.
10208795 - Statistics of sexual size dimorphism.
25091065 - A crystallographic perspective on sharing data and knowledge.
19487485 - Everybody loves me: self-evaluations and metaperceptions of dating popularity.
Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Statistics in medicine     Volume:  24     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2005 Nov 
Date Detail:
Created Date:  2005-10-13     Completed Date:  2006-01-13     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  3319-29     Citation Subset:  IM    
Department of Public Health Sciences, Wake Forest University, Winston Salem, NC 27157, USA.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Body Mass Index
Computer Simulation
Data Interpretation, Statistical*
Logistic Models*
Nutrition Surveys
Sampling Studies*
United States

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Familial clustering of beta-cell autoimmunity in initially non-diabetic children.
Next Document:  Utilization of self-sorting processes to generate dynamic combinatorial libraries with new network t...