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Testing quasi-independence for doubly truncated data

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  • Pao-Sheng Shen

Abstract

Doubly truncated data appear in a number of applications, including astronomy and survival analysis. Quasi-independence is a common assumption for analysing double-truncated data. To verify this condition, using the approach of Emura and Wang [(2010), ‘Testing Quasi-independence for Truncation Data’, Journal of Multivariate Analysis, 101, 223–293], we propose a class of weighted log-rank-type statistics. The asymptotic distribution theory of the test is presented. The performance of the proposed test is compared with the existing test proposed by Martin and Betensky [(2005), ‘Testing Quasi-independence of Failure and Truncation Via Conditional Kendall's Tau’, Journal of the American Statistical Association, 100, 484–492], by means of Monte Carlo simulations.

Suggested Citation

  • Pao-Sheng Shen, 2011. "Testing quasi-independence for doubly truncated data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 753-761.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:3:p:753-761
    DOI: 10.1080/10485252.2011.564280
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    References listed on IDEAS

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    1. Lajmi Lakhal Chaieb & Louis-Paul Rivest & Belkacem Abdous, 2006. "Estimating survival under a dependent truncation," Biometrika, Biometrika Trust, vol. 93(3), pages 655-669, September.
    2. Carla Moreira & Jacobo de Uña-Álvarez, 2010. "Bootstrapping the NPMLE for doubly truncated data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(5), pages 567-583.
    3. Martin, Emily C. & Betensky, Rebecca A., 2005. "Testing Quasi-Independence of Failure and Truncation Times via Conditional Kendall's Tau," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 484-492, June.
    4. Emura, Takeshi & Wang, Weijing, 2010. "Testing quasi-independence for truncation data," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 223-239, January.
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    Cited by:

    1. Takeshi Emura & Ya-Hsuan Hu & Yoshihiko Konno, 2017. "Asymptotic inference for maximum likelihood estimators under the special exponential family with double-truncation," Statistical Papers, Springer, vol. 58(3), pages 877-909, September.
    2. Achim Dörre & Chung-Yan Huang & Yi-Kuan Tseng & Takeshi Emura, 2021. "Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model," Computational Statistics, Springer, vol. 36(1), pages 375-408, March.

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