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A goodness of fit test for left-truncated and right-censored data

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  • Hwang, Yi-Ting
  • Wang, Chun-chao

Abstract

Survival data in many follow-up studies are often collected using cross-sectional sampling designs. Data of this type are often subject to left-truncation and right-censoring. The product limit estimator is the most commonly used nonparametric estimator for the variable of interest. However, under certain assumptions, it is known to be less efficient than the parametric or semiparametric estimator. A chi-square test is proposed for testing the hypothesis that the truncation distribution follows a parametric family.

Suggested Citation

  • Hwang, Yi-Ting & Wang, Chun-chao, 2008. "A goodness of fit test for left-truncated and right-censored data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2420-2425, October.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:15:p:2420-2425
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    References listed on IDEAS

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    1. He, Shuyuan & Yang, Grace L., 2000. "On the strong convergence of the product-limit estimator and its integrals under censoring and random truncation," Statistics & Probability Letters, Elsevier, vol. 49(3), pages 235-244, September.
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    Cited by:

    1. Ahmadi, Jafar & Doostparast, Mahdi & Parsian, Ahmad, 2012. "Estimation with left-truncated and right censored data: A comparison study," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1391-1400.
    2. Wang, Liang & Tripathi, Yogesh Mani & Dey, Sanku & Zhang, Chunfang & Wu, Ke, 2022. "Analysis of dependent left-truncated and right-censored competing risks data with partially observed failure causes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 285-307.
    3. Shen, Pao-sheng, 2009. "Semiparametric analysis of survival data with left truncation and right censoring," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4417-4432, October.
    4. N. Davarzani & L. Golparvar & A. Parsian & R. Peeters, 2017. "Estimation on dependent right censoring scheme in an ordinary bivariate geometric distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1369-1384, June.

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