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June 2020 Seasonal warranty prediction based on recurrent event data
Qianqian Shan, Yili Hong, William Q. Meeker
Ann. Appl. Stat. 14(2): 929-955 (June 2020). DOI: 10.1214/20-AOAS1333


Warranty return data from repairable systems, such as home appliances, lawn mowers, computers and automobiles, result in recurrent event data. The nonhomogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. The methods presented here, however, can be applied to other applications that result in seasonal recurrent event data. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the overall population. The stratification facilitates modeling the recurrent event data with both time-varying and time-constant covariates. We demonstrate and validate the models using warranty claims data for two different types of products. The results show that our approach provides important improvements in the predictive power of monthly events compared with models that do not take the seasonality and covariates into account.


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Qianqian Shan. Yili Hong. William Q. Meeker. "Seasonal warranty prediction based on recurrent event data." Ann. Appl. Stat. 14 (2) 929 - 955, June 2020.


Received: 1 February 2019; Revised: 1 February 2020; Published: June 2020
First available in Project Euclid: 29 June 2020

zbMATH: 07239890
MathSciNet: MR4117835
Digital Object Identifier: 10.1214/20-AOAS1333

Keywords: EM algorithm , hierarchical clustering , missing data , NHPP , random effects , seasonal dynamic covariates

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.14 • No. 2 • June 2020
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