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Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals

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  • Lin, Kunsong
  • Chen, Yunxia

Abstract

This paper conducts a two-dimensional warranty analysis on the data with heterogeneity in terms of both age and usage. Two models are developed to evaluate the severity of warranty claims. The first one uses a copula to describe the global dependence between the age and usage populations, whereas the second one further captures various local dependence in different warranty regions. Since the mixing proportions regarding heterogeneity are latent, the expectation maximization algorithm is proposed to estimate parameters and classify the warranty data. The likelihood ratio test is further utilized to determine the number of subpopulations. A comparison with two other models shows the superiority of the proposed one in terms of the Akaike information criterion. It is found that more than 85% of the claims are classified into normal failures, and local dependence structures might vary from a symmetric one to an asymmetric one. Meanwhile, as the sample size decreases, the heterogeneity of usage would vanish, and some local dependence structures would change.

Suggested Citation

  • Lin, Kunsong & Chen, Yunxia, 2021. "Analysis of two-dimensional warranty data considering global and local dependence of heterogeneous marginals," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308206
    DOI: 10.1016/j.ress.2020.107327
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    References listed on IDEAS

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    Cited by:

    1. Huang, Yeu-Shiang & Fang, Chih-Chiang & Lu, Chang-Ming & (Bill) Tseng, Tzu-Liang, 2022. "Optimal Warranty Policy for Consumer Electronics with Dependent Competing Failure Processes," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Gupta, Sanjib Kumar & Bhattacharya, Debasis, 2022. "Non-parametric estimation of bivariate reliability from incomplete two-dimensional warranty data," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    4. Li, Ting & He, Shuguang & Zhao, Xiujie, 2022. "Optimal warranty policy design for deteriorating products with random failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Gupta, Sanjib Kumar & Chattopadhyay, Gaurangadeb, 2022. "Early detection of reliability related problems from two-dimensional warranty data considering labour code priority index," Reliability Engineering and System Safety, Elsevier, vol. 225(C).

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