IDEAS home Printed from https://ideas.repec.org/a/spr/sankhb/v83y2021i2d10.1007_s13571-019-00214-w.html
   My bibliography  Save this article

A Weighted Likelihood Approach to Problems in Survival Data

Author

Listed:
  • Adhidev Biswas

    (Indian Statistical Institute)

  • Suman Majumder

    (North Carolina State University)

  • Pratim Guha Niyogi

    (Michigan State University)

  • Ayanendranath Basu

    (Indian Statistical Institute)

Abstract

This work is motivated by the need to perform the appropriate “robust” analysis on right-censored survival data. As in other domains of application, modelling and analysis of data generated by medical and biological studies are often unstable due to the presence of outliers and model misspecification. Use of robust techniques is helpful in this respect, and has often been the default in such situations. However, a large contaminating set of observations can often mean that the group is generated systematically by a model which is different from the one to which the majority of the data are attributed, rather than being stray outliers. The method of weighted likelihood estimating equations might provide a solution to this problem, where the different roots obtained can indicate the presence of distinct parametric clusters, rather than providing a single robust fit which ignores the observations incompatible with the major fitted component. Efron’s (J. Am. Stat. Assoc. 83, 402, 414–425, 1988) head-and-neck cancer data provide an ideal scenario for the application of such a method. A recently developed variant of the weighted likelihood method provides a nice illustration of the presence of different clusters in Efron’s data, and highlights the benefits of the weighted likelihood method in relation to classical robust techniques.

Suggested Citation

  • Adhidev Biswas & Suman Majumder & Pratim Guha Niyogi & Ayanendranath Basu, 2021. "A Weighted Likelihood Approach to Problems in Survival Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 466-492, November.
  • Handle: RePEc:spr:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-019-00214-w
    DOI: 10.1007/s13571-019-00214-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13571-019-00214-w
    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/s13571-019-00214-w?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. Basu, Ayanendranath & Lindsay, Bruce G., 2004. "The iteratively reweighted estimating equation in minimum distance problems," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 105-124, March.
    2. Srabashi Basu & Ayanendranath Basu & M. Jones, 2006. "Robust and Efficient Parametric Estimation for Censored Survival Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 341-355, June.
    3. Christian Léger & Joseph Romano, 1990. "Bootstrap choice of tuning parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(4), pages 709-735, December.
    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. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.
    2. Mandal, Abhijit & Basu, Ayanendranath, 2013. "Minimum disparity estimation: Improved efficiency through inlier modification," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 71-86.
    3. R. Bajorunaite & V. Brazauskas, 2008. "Method of trimmed moments for robust fitting of parametric failure time models," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 341-360.
    4. Zhan, Tingting & Chevoneva, Inna & Iglewicz, Boris, 2011. "Generalized weighted likelihood density estimators with application to finite mixture of exponential family distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 457-465, January.
    5. Cao, Ricardo & Cuevas, Antonio & Fraiman, Ricardo, 1995. "Minimum distance density-based estimation," Computational Statistics & Data Analysis, Elsevier, vol. 20(6), pages 611-631, December.
    6. Ferrari, Davide & Zheng, Chao, 2016. "Reliable inference for complex models by discriminative composite likelihood estimation," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 68-80.
    7. Abhik Ghosh, 2022. "Robust parametric inference for finite Markov chains," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 118-147, March.
    8. Suman Majumder & Adhidev Biswas & Tania Roy & Subir Kumar Bhandari & Ayanendranath Basu, 2021. "Statistical inference based on a new weighted likelihood approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 97-120, January.
    9. Wang, Yong, 2007. "Minimum disparity computation via the iteratively reweighted least integrated squares algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5662-5672, August.
    10. Demetrescu, Matei, 2006. "An extension of the Gauss-Newton algorithm for estimation under asymmetric loss," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 379-401, January.
    11. Pierre‐Yves Deléamont & Elvezio Ronchetti, 2022. "Robust inference with censored survival data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1496-1533, December.

    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:sankhb:v:83:y:2021:i:2:d:10.1007_s13571-019-00214-w. 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.