Objective Bayesian Inference for Repairable System Subject to Competing Risks

by   Marco Pollo, et al.
Universidade de São Paulo

Competing risks models for a repairable system subject to several failure modes are discussed. Under minimal repair, it is assumed that each failure mode has a power law intensity. An orthogonal reparametrization is used to obtain an objective Bayesian prior which is invariant under relabelling of the failure modes. The resulting posterior is a product of gamma distributions and has appealing properties: one-to-one invariance, consistent marginalization and consistent sampling properties. Moreover, the resulting Bayes estimators have closed-form expressions and are naturally unbiased for all the parameters of the model. The methodology is applied in the analysis of (i) a previously unpublished dataset about recurrent failure history of a sugarcane harvester and (ii) records of automotive warranty claims introduced in [1]. A simulation study was carried out to study the efficiency of the methods proposed.


page 1

page 2

page 3

page 4


Bayesian Inference of a Dependent Competing Risk Data

Analysis of competing risks data plays an important role in the lifetime...

A Model for Censored Reliability Data with Two Dependent Failure Modes and Prediction of Future Failures

Quite often, we observe reliability data with two failure modes that may...

Order Restricted Inference for Adaptive Progressively Censored Competing Risks Data

Under adaptive progressive Type-II censoring schemes, order restricted i...

Power divergence approach for one-shot device testing under competing risks

Most work on one-shot devices assume that there is only one possible cau...

On ageing properties of lifetime distributions

A reasonable segment of reliability theory is perpetrated to the study o...

Destructive cure models with proportional hazards lifetimes and associated likelihood inference

In survival analysis, cure models have gained much importance due to rap...

Bayesian taut splines for estimating the number of modes

The number of modes in a probability density function is representative ...

Please sign up or login with your details

Forgot password? Click here to reset