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Improved on-line estimation for gamma process

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  • Xu, Ancha
  • Shen, Lijuan

Abstract

In this paper, two new estimators based on the spirit of the best linear unbiased estimators are separately developed for homogeneous gamma process. Both estimators can be computed recursively, and have high efficiency. We compare the two new estimators with those of Paroissin (2017) by simulations, and find that ours have smaller biases and mean squared errors.

Suggested Citation

  • Xu, Ancha & Shen, Lijuan, 2018. "Improved on-line estimation for gamma process," Statistics & Probability Letters, Elsevier, vol. 143(C), pages 67-73.
  • Handle: RePEc:eee:stapro:v:143:y:2018:i:c:p:67-73
    DOI: 10.1016/j.spl.2018.07.021
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    References listed on IDEAS

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    1. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    2. Peng, Weiwen & Li, Yan-Feng & Yang, Yuan-Jian & Huang, Hong-Zhong & Zuo, Ming J., 2014. "Inverse Gaussian process models for degradation analysis: A Bayesian perspective," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 175-189.
    3. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne, 2016. "Remaining useful lifetime estimation and noisy gamma deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 76-87.
    4. Guida, Maurizio & Postiglione, Fabio & Pulcini, Gianpaolo, 2015. "A random-effects model for long-term degradation analysis of solid oxide fuel cells," Reliability Engineering and System Safety, Elsevier, vol. 140(C), pages 88-98.
    5. Wang, Pingping & Tang, Yincai & Joo Bae, Suk & He, Yong, 2018. "Bayesian analysis of two-phase degradation data based on change-point Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 244-256.
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    Cited by:

    1. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2020. "Mis-specification analysis of Wiener degradation models by using f-divergence with outliers," Reliability Engineering and System Safety, Elsevier, vol. 195(C).

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