IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v76y2020i2p575-577.html
   My bibliography  Save this article

Rejoinder to “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test”

Author

Listed:
  • Giovanni Nattino
  • Michael L. Pennell
  • Stanley Lemeshow

Abstract

No abstract is available for this item.

Suggested Citation

  • Giovanni Nattino & Michael L. Pennell & Stanley Lemeshow, 2020. "Rejoinder to “Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer‐Lemeshow test”," Biometrics, The International Biometric Society, vol. 76(2), pages 575-577, June.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:2:p:575-577
    DOI: 10.1111/biom.13250
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13250
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13250?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
    ---><---

    Citations

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


    Cited by:

    1. Daniel Fernández & Louise McMillan & Richard Arnold & Martin Spiess & Ivy Liu, 2022. "Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model," Stats, MDPI, vol. 5(2), pages 1-14, June.
    2. Rui Liu & Feng Tan & Yaxuan Wang & Bo Ma & Ming Yuan & Lianxia Wang & Xin Zhao, 2022. "Machine Learning Identification of Saline-Alkali-Tolerant Japonica Rice Varieties Based on Raman Spectroscopy and Python Visual Analysis," Agriculture, MDPI, vol. 12(7), pages 1-14, July.
    3. Timo Dimitriadis & Lutz Duembgen & Alexander Henzi & Marius Puke & Johanna Ziegel, 2022. "Honest calibration assessment for binary outcome predictions," Papers 2203.04065, arXiv.org, revised Nov 2022.
    4. M. Kelemen & J. Danesh & E. Angelantonio & M. Inouye & J. O’Sullivan & L. Pennells & T. Roychowdhury & M. J. Sweeting & A. M. Wood & S. Harrison & L. G. Kim, 2024. "Evaluating the cost-effectiveness of polygenic risk score-stratified screening for abdominal aortic aneurysm," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Stamatiadou, Valentini & Mazaris, Antonios & Mallios, Zisis & Katsanevakis, Stelios, 2023. "Valuation and mapping of the recreational diving ecosystem service of the Aegean Sea," Ecosystem Services, Elsevier, vol. 64(C).

    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:bla:biomet:v:76:y:2020:i:2:p:575-577. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.