IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v42y2015i7p1442-1457.html
   My bibliography  Save this article

A software reliability model using mean residual quantile function

Author

Listed:
  • Bijamma Thomas
  • N.N. Midhu
  • P.G. Sankaran

Abstract

In this paper, we propose a class of distributions with the inverse linear mean residual quantile function. The distributional properties of the family of distributions are studied. We then discuss the reliability characteristics of the family of distributions. Some characterizations of the class of distributions are also discussed. The parameters of the class of distributions are estimated using the method of L-moments. The proposed class of distributions is applied to a real data set.

Suggested Citation

  • Bijamma Thomas & N.N. Midhu & P.G. Sankaran, 2015. "A software reliability model using mean residual quantile function," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1442-1457, July.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1442-1457
    DOI: 10.1080/02664763.2014.1000273
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2014.1000273
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2014.1000273?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. N.N. Midhu & P.G. Sankaran & N. Unnikrishnan Nair, 2014. "A Class of Distributions with Linear Hazard Quantile Function," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(17), pages 3674-3689, September.
    2. Unnikrishnan Nair, N. & Vineshkumar, B., 2011. "Ageing concepts: An approach based on quantile function," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 2016-2025.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tapan Kumar Chakrabarty & Dreamlee Sharma, 2021. "A Generalization of the Quantile-Based Flattened Logistic Distribution," Annals of Data Science, Springer, vol. 8(3), pages 603-627, September.

    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. P. Sankaran & Bijamma Thomas & N. Midhu, 2015. "On bilinear hazard quantile functions," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 135-148, April.
    2. P.G. Sankaran & N.N. Midhu, 2017. "Nonparametric estimation of mean residual quantile function under right censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1856-1874, July.
    3. P. Sankaran & N. Midhu, 2016. "Testing exponentiality using mean residual quantile function," Statistical Papers, Springer, vol. 57(1), pages 235-247, March.
    4. Sreelakshmi N. & Asha G. & Muraleedharan Nair K. R., 2015. "On Inferring Income Inequality Measures Using L-moments," Stochastics and Quality Control, De Gruyter, vol. 30(2), pages 75-87, December.
    5. Kayal, Suchandan, 2018. "Quantile-based cumulative inaccuracy measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 329-344.
    6. Perepolkin, Dmytro & Goodrich, Benjamin & Sahlin, Ullrika, 2021. "The tenets of indirect inference in Bayesian models," OSF Preprints enzgs, Center for Open Science.
    7. Sankaran, P.G. & Sunoj, S.M. & Nair, N. Unnikrishnan, 2016. "Kullback–Leibler divergence: A quantile approach," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 72-79.
    8. Tapan Kumar Chakrabarty & Dreamlee Sharma, 2021. "A Generalization of the Quantile-Based Flattened Logistic Distribution," Annals of Data Science, Springer, vol. 8(3), pages 603-627, September.

    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:taf:japsta:v:42:y:2015:i:7:p:1442-1457. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.