More efficient logistic analysis using moving extreme ranked set sampling

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 (MERSS min ) 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

Fingerprint

Ranked Set Sampling
Logistics
Extremes
Logistic Regression
Auxiliary Variables
Odds Ratio
Public Health
Regression Analysis
Surveillance
Parameter Estimation
Ranking
Simulation Study
Testing
Dependent
Modeling
Demonstrate
Sampling

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

More efficient logistic analysis using moving extreme ranked set sampling. / Samawi, Hani M.; Rochani, Haresh; Linder, Daniel F; Chatterjee, Arpita.

In: Journal of Applied Statistics, Vol. 44, No. 4, 12.03.2017, p. 753-766.

Research output: Contribution to journalArticle

Samawi, Hani M. ; Rochani, Haresh ; Linder, Daniel F ; Chatterjee, Arpita. / More efficient logistic analysis using moving extreme ranked set sampling. In: Journal of Applied Statistics. 2017 ; Vol. 44, No. 4. pp. 753-766.
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