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On nonparametric maximum likelihood estimation with interval censoring and left truncation

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  • Michael G. Hudgens

Abstract

Summary. A graph theoretical approach is employed to describe the support set of the nonparametric maximum likelihood estimator for the cumulative distribution function given interval‐censored and left‐truncated data. A necessary and sufficient condition for the existence of a nonparametric maximum likelihood estimator is then derived. Two previously analysed data sets are revisited.

Suggested Citation

  • Michael G. Hudgens, 2005. "On nonparametric maximum likelihood estimation with interval censoring and left truncation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(4), pages 573-587, September.
    • Handle: RePEc:bla:jorssb:v:67:y:2005:i:4:p:573-587
    DOI: 10.1111/j.1467-9868.2005.00516.x
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    Cited by:

    1. Pao-sheng Shen, 2013. "Regression analysis of interval censored and doubly truncated data with linear transformation models," Computational Statistics, Springer, vol. 28(2), pages 581-596, April.
    2. Pao-Sheng Shen, 2020. "Nonparametric estimators of survival function under the mixed case interval-censored model with left truncation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 624-637, July.
    3. Fei Gao & Kwun Chuen Gary Chan, 2019. "Semiparametric regression analysis of length‐biased interval‐censored data," Biometrics, The International Biometric Society, vol. 75(1), pages 121-132, March.
    4. Pao-sheng Shen, 2022. "Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring," Computational Statistics, Springer, vol. 37(1), pages 29-42, March.
    5. Ana Belén Ramos-Guajardo, 2022. "A hierarchical clustering method for random intervals based on a similarity measure," Computational Statistics, Springer, vol. 37(1), pages 229-261, March.

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