ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

Field Failure Forecasting Using Warranty Claim Data

13 September 2016, 14:50 – 15:10

Abstract

Submitted by
Antonio Pievatolo
Authors
Antonio Pievatolo (CNR-IMATI)
Abstract
Bayesian methods provide a natural framework for the analysis of data that become sequentially available, either for descriptive, predictive or prescriptive purposes. We present a methodology for the prediction of future claims, based on a nonlinear dynamic model, with Poisson observable counts and dynamic failure rates. An error-propagation approximation makes it possible to assign a meaningful covariance structure to failure rates, resulting in a dynamic Poisson-Lognormal model. An adaptation of the auxiliary particle filter similar to the particle learning algorithm is used for parameter learning and forecasting. A series of examples with real datasets show that it is possible to obtain a small forecasting error for claims having very different patterns.
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