IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v35y2019i3p837-857.html
   My bibliography  Save this article

Detecting change points in the stress‐strength reliability P(X

Author

Listed:
  • Hang Xu
  • Philip L.H. Yu
  • Mayer Alvo

Abstract

We address the statistical problem of detecting change points in the stress‐strength reliability R=P(X

Suggested Citation

  • Hang Xu & Philip L.H. Yu & Mayer Alvo, 2019. "Detecting change points in the stress‐strength reliability P(X," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 837-857, May.
    • Handle: RePEc:wly:apsmbi:v:35:y:2019:i:3:p:837-857
    DOI: 10.1002/asmb.2413
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2413
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2413?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ross, Gordon J., 2015. "Parametric and Nonparametric Sequential Change Detection in R: The cpm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i03).
    2. James, Nicholas A. & Matteson, David S., 2015. "ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i07).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    2. Bill Russell & Dooruj Rambaccussing, 2019. "Breaks and the statistical process of inflation: the case of estimating the ‘modern’ long-run Phillips curve," Empirical Economics, Springer, vol. 56(5), pages 1455-1475, May.
    3. Park, Beum-Jo, 2022. "The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market," Research in International Business and Finance, Elsevier, vol. 59(C).
    4. Andreas Anastasiou & Piotr Fryzlewicz, 2022. "Detecting multiple generalized change-points by isolating single ones," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 141-174, February.
    5. Arjun Prakash & Nick James & Max Menzies & Gilad Francis, 2020. "Structural clustering of volatility regimes for dynamic trading strategies," Papers 2004.09963, arXiv.org, revised Nov 2021.
    6. Yanlin Shi, 2023. "Long memory and regime switching in the stochastic volatility modelling," Annals of Operations Research, Springer, vol. 320(2), pages 999-1020, January.
    7. Corbet, Shaen & Lucey, Brian & Peat, Maurice & Vigne, Samuel, 2018. "Bitcoin Futures—What use are they?," Economics Letters, Elsevier, vol. 172(C), pages 23-27.
    8. James, Nick & Menzies, Max & Chan, Jennifer, 2021. "Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    9. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    10. Brice B. Hanberry, 2021. "Timing of Tree Density Increases, Influence of Climate Change, and a Land Use Proxy for Tree Density Increases in the Eastern United States," Land, MDPI, vol. 10(11), pages 1-17, October.
    11. Peter M C Harrison & Roberta Bianco & Maria Chait & Marcus T Pearce, 2020. "PPM-Decay: A computational model of auditory prediction with memory decay," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-41, November.
    12. Magda Monteiro & Marco Costa, 2023. "Change Point Detection by State Space Modeling of Long-Term Air Temperature Series in Europe," Stats, MDPI, vol. 6(1), pages 1-18, January.
    13. Nora M. Villanueva & Marta Sestelo & Miguel M. Fonseca & Javier Roca-Pardiñas, 2023. "seq2R: An R Package to Detect Change Points in DNA Sequences," Mathematics, MDPI, vol. 11(10), pages 1-20, May.
    14. Lykou, R. & Tsaklidis, G. & Papadimitriou, E., 2020. "Change point analysis on the Corinth Gulf (Greece) seismicity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    15. Rui Qiang & Eric Ruggieri, 2023. "Autocorrelation and Parameter Estimation in a Bayesian Change Point Model," Mathematics, MDPI, vol. 11(5), pages 1-22, February.
    16. Mengjia Yu & Xiaohui Chen, 2021. "Finite sample change point inference and identification for high‐dimensional mean vectors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 247-270, April.
    17. Nick James & Max Menzies & Jennifer Chan, 2023. "Semi-Metric Portfolio Optimization: A New Algorithm Reducing Simultaneous Asset Shocks," Econometrics, MDPI, vol. 11(1), pages 1-33, March.
    18. Peter Nystrup & Bo William Hansen & Henrik Madsen & Erik Lindström, 2016. "Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 361-374, September.
    19. Michele Scagliarini & Rosanna Gualdi & Giuseppe Ottaviano & Antonietta Rizzo, 2023. "Detection of anomalous radioxenon concentrations: A distribution‐free approach," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
    20. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:apsmbi:v:35:y:2019:i:3:p:837-857. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.