Bayesian nonparametric models for ranked set sampling
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| 003 | CHVBK | ||
| 005 | 20210128100355.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150401xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10985-014-9312-x |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10985-014-9312-x | ||
| 245 | 0 | 0 | |a Bayesian nonparametric models for ranked set sampling |h [Elektronische Daten] |c [Nader Gemayel, Elizabeth Stasny, Douglas Wolfe] |
| 520 | 3 | |a 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). | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a ANOVA decomposition |2 nationallicence | |
| 690 | 7 | |a Dependent Dirichlet process |2 nationallicence | |
| 690 | 7 | |a Imperfect ranking |2 nationallicence | |
| 690 | 7 | |a Judgment order statistics |2 nationallicence | |
| 690 | 7 | |a Judgment post-stratification |2 nationallicence | |
| 690 | 7 | |a Markov chain Monte Carlo |2 nationallicence | |
| 700 | 1 | |a Gemayel |D Nader |u JPMorgan Chase, Columbus, OH, USA |4 aut | |
| 700 | 1 | |a Stasny |D Elizabeth |u Department of Statistics, Ohio State University, 43210, Columbus, OH, USA |4 aut | |
| 700 | 1 | |a Wolfe |D Douglas |u Department of Statistics, Ohio State University, 43210, Columbus, OH, USA |4 aut | |
| 773 | 0 | |t Lifetime Data Analysis |d Springer US; http://www.springer-ny.com |g 21/2(2015-04-01), 315-329 |x 1380-7870 |q 21:2<315 |1 2015 |2 21 |o 10985 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10985-014-9312-x |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10985-014-9312-x |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gemayel |D Nader |u JPMorgan Chase, Columbus, OH, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Stasny |D Elizabeth |u Department of Statistics, Ohio State University, 43210, Columbus, OH, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Wolfe |D Douglas |u Department of Statistics, Ohio State University, 43210, Columbus, OH, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Lifetime Data Analysis |d Springer US; http://www.springer-ny.com |g 21/2(2015-04-01), 315-329 |x 1380-7870 |q 21:2<315 |1 2015 |2 21 |o 10985 | ||