Efficient Estimation of a Scale Parameter of Bivariate Lomax Distribution by Ranked Set Sampling

Abstract

Ranked set sampling (RSS) is an efficient technique for estimating parameters and is applicable whenever ranking on a set of sampling units can be done easily by a judgment method or based on an auxiliary variable. In this paper, we assume [Formula: see text]to have bivariate Lomax distribution where a study variable [Formula: see text]is difficult and/or expensive to measure and is correlated with an auxiliary variable [Formula: see text] which is readily measurable. The auxiliary variable is used to rank the sampling units. In this article, we propose an estimator for the scale parameter of bivariate Lomax distribution using some of the modified RSS schemes. Efficiency comparison of the proposed estimators is performed numerically as well as graphically. A simulation study is also performed to demonstrate the performance of the proposed estimators. Finally, we implement the results to real-life datasets. AMS classification codes: 62D05, 62F07, 62G30

Inference of Constant-Stress Model of Fréchet Distribution under a Maximum Ranked Set Sampling with Unequal Samples

“…In addition, various authors have also demonstrated that the RSS gives more inferential efficiency than SRS from different perspectives (e.g., Almanjahie et al [10], Al-Omari et al [11], Yao et al [12], Aljohani [13]). For some recent contributions, one could also refer to the works of Koshti and Kamalja [14], Sabry and Shaaban [15] and Bhushan and Kumar [16] among others.…”