# Bayesian Prediction of Weibull Distribution Based on Fixed and Random Sample Size

Serdica Mathematical Journal (2009)

- Volume: 35, Issue: 2, page 129-146
- ISSN: 1310-6600

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topEllah, A. H. Abd. "Bayesian Prediction of Weibull Distribution Based on Fixed and Random Sample Size." Serdica Mathematical Journal 35.2 (2009): 129-146. <http://eudml.org/doc/281516>.

@article{Ellah2009,

abstract = {2000 Mathematics Subject Classification: 62E16, 65C05, 65C20.We consider the problem of predictive interval for future observations from Weibull distribution. We consider two cases they are: (i) fixed sample size (FSS), (ii) random sample size (RSS). Further, we derive the predictive function for both FSS and RSS in closed forms. Next, the upper and lower 1%, 2.5%, 5% and 10% critical points for the predictive functions are calculated. To show the usefulness of our results, we present some simulation examples. Finally, we apply our results to some real data set in life testing given in Lawless [16].},

author = {Ellah, A. H. Abd},

journal = {Serdica Mathematical Journal},

keywords = {Predictive Function; Random Sample Size; Predictive Intervals; Bayesian Prediction; order statistics; predictive function; predictive intervals},

language = {eng},

number = {2},

pages = {129-146},

publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},

title = {Bayesian Prediction of Weibull Distribution Based on Fixed and Random Sample Size},

url = {http://eudml.org/doc/281516},

volume = {35},

year = {2009},

}

TY - JOUR

AU - Ellah, A. H. Abd

TI - Bayesian Prediction of Weibull Distribution Based on Fixed and Random Sample Size

JO - Serdica Mathematical Journal

PY - 2009

PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences

VL - 35

IS - 2

SP - 129

EP - 146

AB - 2000 Mathematics Subject Classification: 62E16, 65C05, 65C20.We consider the problem of predictive interval for future observations from Weibull distribution. We consider two cases they are: (i) fixed sample size (FSS), (ii) random sample size (RSS). Further, we derive the predictive function for both FSS and RSS in closed forms. Next, the upper and lower 1%, 2.5%, 5% and 10% critical points for the predictive functions are calculated. To show the usefulness of our results, we present some simulation examples. Finally, we apply our results to some real data set in life testing given in Lawless [16].

LA - eng

KW - Predictive Function; Random Sample Size; Predictive Intervals; Bayesian Prediction; order statistics; predictive function; predictive intervals

UR - http://eudml.org/doc/281516

ER -

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