More efficient logistic analysis using moving extreme ranked set sampling

Hani M. Samawi, Haresh Rochani, Daniel Linder, Arpita Chatterjee

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Logistic regression is the most popular technique available for modeling dichotomous-dependent variables. It has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient logistic regression analysis based on moving extreme ranked set sampling (MERSSmin) scheme with ranking based on an easy-to-available auxiliary variable known to be associated with the variable of interest (response variable). The paper demonstrates that this approach will provide more powerful testing procedure as well as more efficient odds ratio and parameter estimation than using simple random sample (SRS). Theoretical derivation and simulation studies will be provided. Real data from 2011 Youth Risk Behavior Surveillance System (YRBSS) data are used to illustrate the procedures developed in this paper.

Original languageEnglish (US)
Pages (from-to)753-766
Number of pages14
JournalJournal of Applied Statistics
Volume44
Issue number4
DOIs
StatePublished - Mar 12 2017
Externally publishedYes

Keywords

  • Ranked set sampling
  • logistic regression
  • moving extreme ranked set sampling
  • odds ratio

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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