IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v45y1999i4p351-358.html
   My bibliography  Save this article

Variance of the bivariate density estimator for left truncated right censored data

Author

Listed:
  • Prewitt, Kathryn
  • Gürler, Ulkü

Abstract

In this study the variance of the bivariate kernel density estimators for the left truncated and right censored (LTRC) observations are considered. In LTRC models, the complete observation of the variable Y is prevented by the truncating variable T and the censoring variable C. Consequently, one observes the i.i.d. samples from the triplets (T,Z,[delta]) only if T[less-than-or-equals, slant]Z, Z=min(Y,C) and [delta] is one if Z=Y and zero otherwise. Gürler and Prewitt (1997, submitted for publication) consider the estimation of the bivariate density function via nonparametric kernel methods and establish an i.i.d. representation of their estimators. Asymptotic variance of the i.i.d. part of their representation is developed in this paper. Application of the results are also discussed for the data-driven and the least-squares cross validation bandwidth choice procedures.

Suggested Citation

  • Prewitt, Kathryn & Gürler, Ulkü, 1999. "Variance of the bivariate density estimator for left truncated right censored data," Statistics & Probability Letters, Elsevier, vol. 45(4), pages 351-358, December.
  • Handle: RePEc:eee:stapro:v:45:y:1999:i:4:p:351-358
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(99)00077-2
    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. Gijbels, I. & Wang, J. L., 1993. "Strong Representations of the Survival Function Estimator for Truncated and Censored Data with Applications," Journal of Multivariate Analysis, Elsevier, vol. 47(2), pages 210-229, November.
    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. Elisa–María Molanes-López & Ricardo Cao, 2008. "Relative density estimation for left truncated and right censored data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(8), pages 693-720.
    2. Sun, Liuquan & Zhou, Yong, 1998. "Sequential confidence bands for densities under truncated and censored data," Statistics & Probability Letters, Elsevier, vol. 40(1), pages 31-41, September.
    3. BuHamra, Sana S. & Al-Kandari, N.M.Noriah M. & Ahmed, S. E., 2004. "Inference concerning quantile for left truncated and right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 819-831, July.
    4. Gürler, Ülkü & Deniz Yenigün, C., 2011. "Full and conditional likelihood approaches for hazard change-point estimation with truncated and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2856-2870, October.
    5. Sun, Liuquan, 1997. "Bandwidth choice for hazard rate estimators from left truncated and right censored data," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 101-114, December.
    6. Zhao, Mu & Bai, Fangfang & Zhou, Yong, 2011. "Relative deficiency of quantile estimators for left truncated and right censored data," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1725-1732, November.
    7. C. Sánchez-Sellero & W. González-Manteiga & R. Cao, 1999. "Bandwidth Selection in Density Estimation with Truncated and Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(1), pages 51-70, March.
    8. Han-Ying Liang & Jacobo Uña-Álvarez & María Iglesias-Pérez, 2012. "Asymptotic properties of conditional distribution estimator with truncated, censored and dependent data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 790-810, December.
    9. Zohra Guessoum & Abdelkader Tatachak, 2020. "Asymptotic Results for Truncated-censored and Associated Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 142-164, May.
    10. Arcones, Miguel A. & Giné, Evarist, 1995. "On the law of the iterated logarithm for canonical U-statistics and processes," Stochastic Processes and their Applications, Elsevier, vol. 58(2), pages 217-245, August.
    11. Shi, Jianhua & Chen, Xiaoping & Zhou, Yong, 2015. "The strong representation for the nonparametric estimator of length-biased and right-censored data," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 49-57.
    12. Fermanian, Jean-David, 1997. "Multivariate Hazard Rates under Random Censorship," Journal of Multivariate Analysis, Elsevier, vol. 62(2), pages 273-309, August.
    13. 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.
    14. Sun, Liuquan & Zhou, Xian, 2001. "Survival function and density estimation for truncated dependent data," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 47-57, March.
    15. Goele Massonnet & Paul Janssen & Tomasz Burzykowski, 2008. "Fitting Conditional Survival Models to Meta‐Analytic Data by Using a Transformation Toward Mixed‐Effects Models," Biometrics, The International Biometric Society, vol. 64(3), pages 834-842, September.
    16. Zhou, Yong & Yip, Paul S. F., 1999. "A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 261-280, May.
    17. Xun, Li & Shao, Li & Zhou, Yong, 2017. "Efficiency of estimators for quantile differences with left truncated and right censored data," Statistics & Probability Letters, Elsevier, vol. 121(C), pages 29-36.
    18. Gürler, Ülkü & Prewitt, Kathryn, 2000. "Bivariate Density Estimation with Randomly Truncated Data," Journal of Multivariate Analysis, Elsevier, vol. 74(1), pages 88-115, July.
    19. Arthur Berg & Dimitris Politis & Kagba Suaray & Hui Zeng, 2020. "Reduced bias nonparametric lifetime density and hazard estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 704-727, September.
    20. J. M. Vilar & R. Cao & M. C. Ausin & C. Gonzalez-Fragueiro, 2009. "Nonparametric analysis of aggregate loss models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(2), pages 149-166.

    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:45:y:1999:i:4:p:351-358. 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.