Bayesian nonparametric models for ranked set sampling

Abstract

Ranked set sampling (RSS) is a data collection technique that combines measurement with judgment ranking for statistical inference. This paper lays out a formal and natural Bayesian framework for RSS that is analogous to its frequentist justification, and that does not require the assumption of perfect ranking or use of any imperfect ranking models. Prior beliefs about the judgment order statistic distributions and their interdependence are embodied by a nonparametric prior distribution. Posterior inference is carried out by means of Markov chain Monte Carlo techniques, and yields estimators of the judgment order statistic distributions (and of functionals of those distributions).

Exact prediction intervals for future exponential and Pareto lifetimes based on ordered ranked set sampling of non-random and random size

“…Various of these works have used the parametric performance of RSS; see, e.g., Adatia (2000), Alodat and Al-Sagheer (2007), Chen et al (2021), Qian et al (2021), Sadek et al (2015), Shaibu and Muttlak (2004), and Stokes (1995). On the other hand, some other works have used the nonparametric performance of RSS; see, e.g., Bhoj (2016), Bohn (1996), Gemayel et al (2015), and Salehi et al (2015).…”