Journal of Statistical Theory and Applications

Volume 15, Issue 4, December 2016, Pages 345 - 366

One-sequence and two-sequence prediction for future Weibull records

Authors
Omar M. Bdair, Mohammad Z. Raqab
Corresponding Author
Omar M. Bdair
Received 15 May 2015, Accepted 18 June 2016, Available Online 1 December 2016.
DOI
10.2991/jsta.2016.15.4.3How to use a DOI?
Keywords
Weibull distribution; record values; Bayes estimation; Bayes prediction; Monte Carlo samples.
Abstract

Based on record data, prediction of the future records from the two-parameter Weibull distribution is studied. First we consider the sampling based procedure to compute the Bayes estimates and also to construct symmetric credible intervals. Secondly, we consider one-sequence and two-sequence Bayes prediction of the future records based on some observed records. The Monte Carlo algorithms are used to compute simulation consistent predictors and prediction intervals for future unobserved records. A numerical simulation study is conducted to compare the different methods and a real data set involving the annual rainfall recorded at Los Angeles Civic Center during 132 years is analyzed to illustrate the procedures developed here.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Statistical Theory and Applications
Volume-Issue
15 - 4
Pages
345 - 366
Publication Date
2016/12/01
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
10.2991/jsta.2016.15.4.3How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Omar M. Bdair
AU  - Mohammad Z. Raqab
PY  - 2016
DA  - 2016/12/01
TI  - One-sequence and two-sequence prediction for future Weibull records
JO  - Journal of Statistical Theory and Applications
SP  - 345
EP  - 366
VL  - 15
IS  - 4
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2016.15.4.3
DO  - 10.2991/jsta.2016.15.4.3
ID  - Bdair2016
ER  -