More efficient logistic analysis using moving extreme ranked set sampling

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.

KEYWORDS:

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

Acknowledgements

The authors, also, would like to thank the associate editor and the reviewers for their important and valuable comments and suggestions which improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.