IDEAS home Printed from https://ideas.repec.org/a/adp/jbboaj/v9y2019i2p34-36.html
   My bibliography  Save this article

Rank Estimators Versus Least Square Estimators for Estimating the Parameters of Semiparametric Accelerated Failure Time Model

Author

Listed:
  • Mostafa Karimi
  • Noor Akma Ibrahim

    (Department of Mathematics, University Putra Malaysia, Malaysia)

Abstract

Rank-based method and least square approach are the most common techniques for estimating the regression parameters of accelerated failure time model. In this paper, both inference procedures are considered, their advantages and disadvantages are explained, and their similarities and differences are discussed.

Suggested Citation

  • Mostafa Karimi & Noor Akma Ibrahim, 2019. "Rank Estimators Versus Least Square Estimators for Estimating the Parameters of Semiparametric Accelerated Failure Time Model," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(2), pages 34-36, February.
    • Handle: RePEc:adp:jbboaj:v:9:y:2019:i:2:p:34-36
    DOI: 10.19080/BBOAJ.2019.09.555757
    as

    Download full text from publisher

    File URL: https://juniperpublishers.com/bboaj/pdf/BBOAJ.MS.ID.555757.pdf
    Download Restriction: no
    File URL: https://juniperpublishers.com/bboaj/BBOAJ.MS.ID.555757.php
    Download Restriction: no
    File URL: https://libkey.io/10.19080/BBOAJ.2019.09.555757?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
    ---><---

    References listed on IDEAS

    as
    1. Zhezhen Jin & D. Y. Lin & Zhiliang Ying, 2006. "On least-squares regression with censored data," Biometrika, Biometrika Trust, vol. 93(1), pages 147-161, March.
    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. Lili Yu & Liang Liu & Ding-Geng(Din) Chen, 2013. "Weighted Least-Squares Method for Right-Censored Data in Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 69(2), pages 358-365, June.
    2. Changrong Yan & Dixin Zhang, 2013. "Sparse dimension reduction for survival data," Computational Statistics, Springer, vol. 28(4), pages 1835-1852, August.
    3. Xu, Linzhi & Zhang, Jiajia, 2010. "An EM-like algorithm for the semiparametric accelerated failure time gamma frailty model," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1467-1474, June.
    4. Lili Yu & Liang Liu & Ding-Geng Chen, 2019. "A homoscedasticity test for the accelerated failure time model," Computational Statistics, Springer, vol. 34(1), pages 433-446, March.
    5. Zou, Yubo & Zhang, Jiajia & Qin, Guoyou, 2011. "A semiparametric accelerated failure time partial linear model and its application to breast cancer," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1479-1487, March.
    6. Lu, Xuewen, 2010. "Asymptotic distributions of two "synthetic data" estimators for censored single-index models," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 999-1015, April.
    7. Bao, Yanchun & He, Shuyuan & Mei, Changlin, 2007. "The Koul-Susarla-Van Ryzin and weighted least squares estimates for censored linear regression model: A comparative study," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6488-6497, August.
    8. Zhou, Mai & Li, Gang, 2008. "Empirical likelihood analysis of the Buckley-James estimator," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 649-664, April.
    9. Yuanshan Wu & Guosheng Yin, 2017. "Multiple imputation for cure rate quantile regression with censored data," Biometrics, The International Biometric Society, vol. 73(1), pages 94-103, March.
    10. Jeongjin Lee & Taehwa Choi & Sangbum Choi, 2024. "Censored broken adaptive ridge regression in high-dimension," Computational Statistics, Springer, vol. 39(6), pages 3457-3482, September.
    11. K. Hendrickx & P. Janssen & A. Verhasselt, 2018. "Penalized spline estimation in varying coefficient models with censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 871-895, December.
    12. Wang, Antai & Zhang, Yilong & Shao, Yongzhao, 2017. "On the likelihood of mixture cure models," Statistics & Probability Letters, Elsevier, vol. 131(C), pages 51-55.
    13. Zhang, Jiajia & Peng, Yingwei, 2009. "Crossing hazard functions in common survival models," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2124-2130, October.
    14. Choi, Taehwa & Kim, Arlene K.H. & Choi, Sangbum, 2021. "Semiparametric least-squares regression with doubly-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    15. Jung-Yu Cheng & Shinn-Jia Tzeng, 2014. "Quantile regression of right-censored length-biased data using the Buckley–James-type method," Computational Statistics, Springer, vol. 29(6), pages 1571-1592, December.
    16. Ersin Yılmaz & Syed Ejaz Ahmed & Dursun Aydın, 2020. "A-Spline Regression for Fitting a Nonparametric Regression Function with Censored Data," Stats, MDPI, vol. 3(2), pages 1-17, May.
    17. Chen, Pengcheng & Zhang, Jiajia & Zhang, Riquan, 2013. "Estimation of the accelerated failure time frailty model under generalized gamma frailty," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 171-180.
    18. Jing Ning & Jing Qin & Yu Shen, 2011. "Buckley–James-Type Estimator with Right-Censored and Length-Biased Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1369-1378, December.
    19. Suraj Peri & Karthik Devarajan & Dong-Hua Yang & Alfred G Knudson & Siddharth Balachandran, 2013. "Meta-Analysis Identifies NF-κB as a Therapeutic Target in Renal Cancer," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    20. Fan, Caiyun & Lu, Wenbin & Zhou, Yong, 2021. "Testing error heterogeneity in censored linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).

    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:adp:jbboaj:v:9:y:2019:i:2:p:34-36. 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: Robert Thomas (email available below). General contact details of provider: .

    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.