TY - JOUR

T1 - Estimation of the reliability parameter for three-parameter Weibull models

AU - Montoya, José A.

AU - Díaz-Francés, Eloísa

AU - P., Gudelia Figueroa

PY - 2019/3/1

Y1 - 2019/3/1

N2 - © 2018 Elsevier Inc. For X and Y independent three-parameter Weibull random variables, the estimation of the reliability parameter δ=P(Y<X) is important in applications of failure times and stress-strength situations in industry, medicine, extreme value theory, hydrology, and environmetrics. Estimation problems arise when the likelihood function is not defined properly, since the three-parameter Weibull distribution is non regular and its density has singularities. Here, a correct likelihood for the three-parameter Weibull case is proposed for the first time, in the spirit of the original definition of likelihood. It takes into account the occurrence of the smallest observation with respect to the threshold parameter, as well as the fact that all measuring instruments necessarily have a finite precision. Possible repeated observations are immediately explained. When the Weibull distributions of X and Y have common threshold and shape parameters, the profile likelihood can be used for inferences about δ. For the case of all three Weibull parameters unknown and arbitrary, inferences about δ are obtained via a novel Bootstrap approach. An example previously analyzed under alternative inferential approaches is presented to illustrate the convenience of the proposal.

AB - © 2018 Elsevier Inc. For X and Y independent three-parameter Weibull random variables, the estimation of the reliability parameter δ=P(Y<X) is important in applications of failure times and stress-strength situations in industry, medicine, extreme value theory, hydrology, and environmetrics. Estimation problems arise when the likelihood function is not defined properly, since the three-parameter Weibull distribution is non regular and its density has singularities. Here, a correct likelihood for the three-parameter Weibull case is proposed for the first time, in the spirit of the original definition of likelihood. It takes into account the occurrence of the smallest observation with respect to the threshold parameter, as well as the fact that all measuring instruments necessarily have a finite precision. Possible repeated observations are immediately explained. When the Weibull distributions of X and Y have common threshold and shape parameters, the profile likelihood can be used for inferences about δ. For the case of all three Weibull parameters unknown and arbitrary, inferences about δ are obtained via a novel Bootstrap approach. An example previously analyzed under alternative inferential approaches is presented to illustrate the convenience of the proposal.

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U2 - 10.1016/j.apm.2018.11.043

DO - 10.1016/j.apm.2018.11.043

M3 - Article

SN - 0307-904X

SP - 621

EP - 633

JO - Applied Mathematical Modelling

JF - Applied Mathematical Modelling

ER -