IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v312y2022i1d10.1007_s10479-019-03165-7.html
   My bibliography  Save this article

Properties and estimation of a bivariate geometric model with locally constant failure rates

Author

Listed:
  • Alessandro Barbiero

    (Università degli Studi di Milano)

Abstract

Stochastic models for correlated count data have been attracting a lot of interest in the recent years, due to their many possible applications: for example, in quality control, marketing, insurance, health sciences, and so on. In this paper, we revise a bivariate geometric model, introduced by Roy (J Multivar Anal 46:362–373, 1993), which is very appealing, since it generalizes the univariate concept of constant failure rate—which characterizes the geometric distribution within the class of all discrete random variables—in two dimensions, by introducing the concept of “locally constant” bivariate failure rates. We mainly focus on four aspects of this model that have not been investigated so far: (1) pseudo-random simulation, (2) attainable Pearson’s correlations, (3) stress–strength reliability parameter, and (4) parameter estimation. A Monte Carlo simulation study is carried out in order to assess the performance of the different estimators proposed and application to real data, along with a comparison with alternative bivariate discrete models, is provided as well.

Suggested Citation

  • Alessandro Barbiero, 2022. "Properties and estimation of a bivariate geometric model with locally constant failure rates," Annals of Operations Research, Springer, vol. 312(1), pages 3-22, May.
  • Handle: RePEc:spr:annopr:v:312:y:2022:i:1:d:10.1007_s10479-019-03165-7
    DOI: 10.1007/s10479-019-03165-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-019-03165-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-019-03165-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lance Fiondella & Panlop Zeephongsekul, 2016. "Trivariate Bernoulli distribution with application to software fault tolerance," Annals of Operations Research, Springer, vol. 244(1), pages 241-255, September.
    2. C. R. Mitchell & A. S. Paulson, 1981. "A new bivariate negative binomial distribution," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 28(3), pages 359-374, September.
    3. Roy, D., 1993. "Reliability Measures in the Discrete Bivariate Set-Up and Related Characterization Results for a Bivariate Geometric Distribution," Journal of Multivariate Analysis, Elsevier, vol. 46(2), pages 362-373, August.
    4. Sun, Kai & Basu, Asit P., 1995. "A characterization of a bivariate geometric distribution," Statistics & Probability Letters, Elsevier, vol. 23(4), pages 307-311, June.
    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. Alessandro Barbiero, 2022. "Discrete analogues of continuous bivariate probability distributions," Annals of Operations Research, Springer, vol. 312(1), pages 23-43, May.
    2. Buddana Amrutha & Kozubowski Tomasz J., 2014. "Discrete Pareto Distributions," Stochastics and Quality Control, De Gruyter, vol. 29(2), pages 143-156, December.
    3. Chénangnon Frédéric Tovissodé & Sèwanou Hermann Honfo & Jonas Têlé Doumatè & Romain Glèlè Kakaï, 2021. "On the Discretization of Continuous Probability Distributions Using a Probabilistic Rounding Mechanism," Mathematics, MDPI, vol. 9(5), pages 1-17, March.
    4. Roy, Dilip & Gupta, R. P., 1999. "Characterizations and model selections through reliability measures in the discrete case," Statistics & Probability Letters, Elsevier, vol. 43(2), pages 197-206, June.
    5. Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2009. "Sufficient reduction in multivariate surveillance," Research Reports 2009:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Barbiero, A., 2019. "A bivariate count model with discrete Weibull margins," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 91-109.
    7. Sellers, Kimberly F. & Morris, Darcy Steeg & Balakrishnan, Narayanaswamy, 2016. "Bivariate Conway–Maxwell–Poisson distribution: Formulation, properties, and inference," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 152-168.
    8. Jafary, Bentolhoda & Mele, Andrew & Fiondella, Lance, 2020. "Component-based system reliability subject to positive and negative correlation," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    9. Hyunju Lee & Ji Hwan Cha, 2020. "A new general class of discrete bivariate distributions constructed by using the likelihood ratio," Statistical Papers, Springer, vol. 61(3), pages 923-944, June.
    10. Marceau, Etienne, 2009. "On the discrete-time compound renewal risk model with dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 245-259, April.
    11. Bilal Ahmad Para & Tariq Rashid Jan, 2019. "On Three Parameter Discrete Generalized Inverse Weibull Distribution: Properties and Applications," Annals of Data Science, Springer, vol. 6(3), pages 549-570, September.
    12. Debasis Kundu, 2020. "On a General Class of Discrete Bivariate Distributions," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 270-304, November.
    13. Dilip Roy, 2002. "On Bivariate Lack of Memory Property and a New Definition," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 404-410, June.
    14. Mathews Joseph & Bhattacharya Sumangal & Das Ishapathik & Sen Sumen, 2022. "Multiple inflated negative binomial regression for correlated multivariate count data," Dependence Modeling, De Gruyter, vol. 10(1), pages 290-307, January.
    15. Andersson, Eva, 2007. "Effect of dependency in systems for multivariate surveillance," Research Reports 2007:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

    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:spr:annopr:v:312:y:2022:i:1:d:10.1007_s10479-019-03165-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.