TY - JOUR
T1 - Prediction of the remaining testing time for the generalized Pareto progressive censoring samples with applications to extreme hydrology events
AU - Raqab, M. Z.
AU - Bdair, O. M.
AU - Madi, M. T.
AU - Alqallaf, F. A.
N1 - Publisher Copyright:
© 2018 Grace Scientific Publishing, LLC.
PY - 2018/4/3
Y1 - 2018/4/3
N2 - The prediction of the unobserved units is typically based on the derivations of the predictive distributions of the individual observations. This technique is of little interest when one wishes to predict a function of missing or unobserved data such as the remaining testing time. In this article, based on a progressive type-II censored sample from the generalized Pareto (GP) distribution, we consider the problem of predicting times to failure of units in multiple stages. Importance sampling is used to estimate the model parameters, and Gibbs and Metropolis samplers are used to predict the testing times of the removed unfailed units. Data analyses involving the water-level exceedances by the River Nidd in North Yorkshire, England, have been performed and predictions of the total remaining level exceedances are discussed.
AB - The prediction of the unobserved units is typically based on the derivations of the predictive distributions of the individual observations. This technique is of little interest when one wishes to predict a function of missing or unobserved data such as the remaining testing time. In this article, based on a progressive type-II censored sample from the generalized Pareto (GP) distribution, we consider the problem of predicting times to failure of units in multiple stages. Importance sampling is used to estimate the model parameters, and Gibbs and Metropolis samplers are used to predict the testing times of the removed unfailed units. Data analyses involving the water-level exceedances by the River Nidd in North Yorkshire, England, have been performed and predictions of the total remaining level exceedances are discussed.
KW - Bayesian estimation
KW - Generalized Pareto distribution
KW - Gibbs and Metropolis sampling prediction
KW - maximum likelihood estimation
KW - progressive censoring samples
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U2 - 10.1080/15598608.2017.1338168
DO - 10.1080/15598608.2017.1338168
M3 - Article
AN - SCOPUS:85025122880
SN - 1559-8608
VL - 12
SP - 165
EP - 187
JO - Journal of Statistical Theory and Practice
JF - Journal of Statistical Theory and Practice
IS - 2
ER -