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Estimating Age Replacement Policies From Small Sample Data

In: Recent Advances In Stochastic Operations Research

Author

Listed:
  • K. RINSAKA

    (Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan)

  • T. DOHI

    (Department of Information Engineering, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan)

Abstract

In the present paper, we consider the typical age replacement models to minimize the relevant expected costs, and formulate the statistical estimation problems with the complete sample of failure time data. Based on the concept of total time on test statistics, we show that the underlying optimization problems are translated to the graphical ones on the data space. Next, we utilize a kernel density estimator and improve the existing statistical estimation algorithms in terms of convergence speed. Throughout simulation experiments, it can be shown that the developed algorithms are useful especially for the small sample problems, and enable us to estimate the optimal age replacement times with higher accuracy.

Suggested Citation

  • K. Rinsaka & T. Dohi, 2007. "Estimating Age Replacement Policies From Small Sample Data," World Scientific Book Chapters, in: Tadashi Dohi & Shunji Osaki & Katsushige Sawaki (ed.), Recent Advances In Stochastic Operations Research, chapter 10, pages 145-158, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812706683_0010
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    Citations

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    Cited by:

    1. Yasuhiro Saito & Tadashi Dohi & Won Y Yun, 2016. "Kernel-based nonparametric estimation methods for a periodic replacement problem with minimal repair," Journal of Risk and Reliability, , vol. 230(1), pages 54-66, February.

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