title: 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).
authors.affiliation: JPMorgan Chase, Columbus, OH, USA.
authors.forename: Nader
authors.initials: N
authors.lastname: Gemayel
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Stasny
authors.forename: Douglas A
authors.initials: DA
authors.lastname: Wolfe
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D018709:Statistics, Nonparametric