IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v61y1999i1p63-84.html
   My bibliography  Save this article

Density and hazard rate estimation for right‐censored data by using wavelet methods

Author

Listed:
  • A. Antoniadis
  • G. Grégoire
  • G. Nason

Abstract

This paper describes a wavelet method for the estimation of density and hazard rate functions from randomly right‐censored data. We adopt a nonparametric approach in assuming that the density and hazard rate have no specific parametric form. The method is based on dividing the time axis into a dyadic number of intervals and then counting the number of events within each interval. The number of events and the survival function of the observations are then separately smoothed over time via linear wavelet smoothers, and then the hazard rate function estimators are obtained by taking the ratio. We prove that the estimators have pointwise and global mean‐square consistency, obtain the best possible asymptotic mean integrated squared error convergence rate and are also asymptotically normally distributed. We also describe simulation experiments that show that these estimators are reasonably reliable in practice. The method is illustrated with two real examples. The first uses survival time data for patients with liver metastases from a colorectal primary tumour without other distant metastases. The second is concerned with times of unemployment for women and the wavelet estimate, through its flexibility, provides a new and interesting interpretation.

Suggested Citation

  • A. Antoniadis & G. Grégoire & G. Nason, 1999. "Density and hazard rate estimation for right‐censored data by using wavelet methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 63-84.
  • Handle: RePEc:bla:jorssb:v:61:y:1999:i:1:p:63-84
    DOI: 10.1111/1467-9868.00163
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9868.00163
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1467-9868.00163?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. Bezandry, Paul H. & Bonney, George E. & Gannoun, Ali, 2005. "Consistent estimation of the density and hazard rate functions for censored data via the wavelet method," Statistics & Probability Letters, Elsevier, vol. 74(4), pages 366-372, October.
    2. Elodie Brunel & Fabienne Comte & Agathe Guilloux, 2008. "Estimation Strategies for Censored Lifetimes with a Lexis‐Diagram Type Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 557-576, September.
    3. Li, Linyuan, 2008. "On the block thresholding wavelet estimators with censored data," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1518-1543, September.
    4. Sam Efromovich & Jufen Chu, 2018. "Hazard rate estimation for left truncated and right censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 889-917, August.
    5. Liang Han-Ying & Mammitzsch Volker & Steinebach Josef, 2005. "Nonlinear wavelet density and hazard rate estimation for censored data under dependent observations," Statistics & Risk Modeling, De Gruyter, vol. 23(3), pages 161-180, March.
    6. Fabienne Comte & Gwennaelle Mabon & Adeline Samson, 2017. "Spline regression for hazard rate estimation when data are censored and measured with error," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(2), pages 115-140, May.
    7. Sam Efromovich, 2016. "Minimax theory of nonparametric hazard rate estimation: efficiency and adaptation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 25-75, February.
    8. Angers, Jean-Francois & MacGibbon, Brenda, 2013. "Hazard function estimation with nonnegative “wavelets”," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 969-978.
    9. Li, Jiexiang & Tran, Lanh Tat, 2007. "Hazard rate estimation on random fields," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1337-1355, August.
    10. Sandra Plancade, 2011. "Model selection for hazard rate estimation in presence of censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(3), pages 313-347, November.
    11. Li, Yunzhe & Lee, Juhee & Kottas, Athanasios, 2024. "Bayesian nonparametric Erlang mixture modeling for survival analysis," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    12. Fabienne Comte & Adeline Samson & Julien J. Stirnemann, 2018. "Hazard estimation with censoring and measurement error: application to length of pregnancy," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 338-359, June.
    13. E. Brunel & F. Comte & A. Guilloux, 2009. "Nonparametric density estimation in presence of bias and censoring," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 166-194, May.
    14. Rigat, F. & Mira, A., 2012. "Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1450-1467.
    15. Liang, Han-Ying & de Uña-Álvarez, Jacobo, 2011. "Wavelet estimation of conditional density with truncated, censored and dependent data," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 448-467, March.

    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:jorssb:v:61:y:1999:i:1:p:63-84. 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: https://edirc.repec.org/data/rssssea.html .

    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.