IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v62y2003i3p229-243.html
   My bibliography  Save this article

Inferences about the scale parameter of the gamma distribution based on data mixed from censoring and grouping

Author

Listed:
  • Mi, Jie
  • Naranjo, Arlene

Abstract

This paper studies the MLE of the scale parameter of the gamma distribution based on data mixed from censoring and grouping when the shape parameter is known. The study shows that under Type I mixed data, the MLE of the scale parameter exists, is unique, and converges almost surely to the true value provided the number of items that fail in the last interval is less than the total number of items placed on test. Under Type II mixed data, these properties hold unconditionally. The relationship between the MLE's based on mixed data and censored data is also examined. An upper bound on the MLE under both Type I and II mixed data is derived to simplify the search for the MLE.

Suggested Citation

  • Mi, Jie & Naranjo, Arlene, 2003. "Inferences about the scale parameter of the gamma distribution based on data mixed from censoring and grouping," Statistics & Probability Letters, Elsevier, vol. 62(3), pages 229-243, April.
    • Handle: RePEc:eee:stapro:v:62:y:2003:i:3:p:229-243
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(03)00005-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Zhenmin Chen & Jie Mi, 2001. "An approximate confidence interval for the scale parameter of the gamma distribution based on grouped data," Statistical Papers, Springer, vol. 42(3), pages 285-299, July.
    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. Wanbo Lu & Tzong-Ru Tsai, 2009. "Interval censored sampling plans for the log-logistic lifetime distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 521-536.
    2. José Dias Curto, 2023. "Inference about the arithmetic average of log transformed data," Statistical Papers, Springer, vol. 64(1), pages 179-204, February.
    3. Lu, Wanbo & Tsai, Tzong-Ru, 2009. "Interval censored sampling plans for the gamma lifetime model," European Journal of Operational Research, Elsevier, vol. 192(1), pages 116-124, January.

    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:eee:stapro:v:62:y:2003:i:3:p:229-243. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.