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.

On Cox proportional hazards model performance under different sampling schemes

“…Helu et al [ 23 ] improved the AFT survival model’s analysis efficiency using a modified version of ERSS, namely ERSSmin (ERSSmax). Also, Samawi et al [ 24 ] used the modified MERSS to improve the logistic regression analyses’ performance. Recently, Samawi et al [ 25 ] further enhances logistic regression analysis using modified DERSS.…”

Estimation of using Modifications of Ranked Set Sampling for Weibull Distribution

“…While sampling units are constituted in the RSS method, due to not doing certain measurements, the possibility of doing error in ranking increases. In order to overcome this problem, various modifications of RSS have been suggested; see Samawi et al (2017), such as extreme ranked set sampling, ERSS (Samavi et al, 1996), median ranked set sampling, MRSS (Muttlak, 1997), double ranked set sampling, DRSS (Al-Saleh and Al- Kadiri, 2000), percentile ranked set sampling, PRSS (Muttlak, 2003), L ranked set sampling, LRSS (Al-Nasser, 2007) and neoteric ranked set sampling, NRSS (Zamanzade and Al-Omari, 2016). Besides these studies, several authors have considered the estimation of the parameters of well-known distributions using RSS or modifications of it.…”