Bayesian Prediction for Progressive Censored Data From the Weibull-Geometric Model
| Autor: | Sara M. A. M. Ali, Z. F. Jaheen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics::Theory
021103 operations research Computer science Applied Mathematics Computation 0211 other engineering and technologies 02 engineering and technology 01 natural sciences General Business Management and Accounting Bayesian Prediction 010104 statistics & probability Bayes' theorem Statistics Statistics::Methodology Applied mathematics Point (geometry) 0101 mathematics Geometric modeling Weibull distribution |
| Zdroj: | American Journal of Mathematical and Management Sciences. 36:247-258 |
| ISSN: | 2325-8454 0196-6324 |
| DOI: | 10.1080/01966324.2017.1334603 |
| Popis: | SYNOPTIC ABSRACTThis article is concerned with the problem of predicting future observables from the Weibull-geometric model based on progressively Type-II censored data. The Bayes point predictors and the Bayesian prediction intervals are obtained. The one and two-sample prediction techniques are considered. Numerical computations are given to illustrate the performance of the procedures. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|