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Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation

Akbarov, Artur, Wu, Shaomin (2012) Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation. Quality and Reliability Engineering International, 28 (6). pp. 663-669. ISSN 0748-8017. (doi:10.1002/qre.1399) (KAR id:31004)


Warranty claims reported in recent months might carry more up-to-date information than those reported in earlier months. Using weighted maximum likelihood estimation for estimating model parameters might therefore lead to better performance of warranty forecasting models than maximum likelihood estimation. This paper examines this issue and also presents comparison of the forecasting performance of the parametric models such as Poisson processes and ARIMA models and non-parametric models such as artificial neural networks. It shows that mixed non-homogenous Poisson process models can lead to better forecasting results than other competing methods. The paper also shows that the models built with the weighted maximum likelihood estimation yield smaller error than those based on the maximum likelihood estimation.

Item Type: Article
DOI/Identification number: 10.1002/qre.1399
Uncontrolled keywords: Overdispersion, Poisson processes, Warranty forecasting, Weighted maximum likelihood method
Subjects: H Social Sciences > HA Statistics > HA33 Management Science
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Shaomin Wu
Date Deposited: 18 Oct 2012 09:34 UTC
Last Modified: 16 Nov 2021 10:08 UTC
Resource URI: (The current URI for this page, for reference purposes)

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