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Conditional Quantile Estimation for Truncated and Associated Data

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  • Latifa Adjoudj
  • Abdelkader Tatachak

Abstract

In survival or reliability studies, it is common to have data which are not only incomplete but weakly dependent too. Random truncation and censoring are two common forms of such data when they are neither independent nor strongly mixing but rather associated. The focus of this paper is on estimating conditional distribution and conditional quantile functions for randomly left truncated data satisfying association condition. We aim at deriving strong uniform consistency rates and asymptotic normality for the estimators and thereby, extend to association case some results stated under iid and α-mixing hypotheses. The performance of the quantile function estimator is evaluated on simulated data sets.

Suggested Citation

  • Latifa Adjoudj & Abdelkader Tatachak, 2019. "Conditional Quantile Estimation for Truncated and Associated Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(18), pages 4598-4641, September.
  • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:18:p:4598-4641
    DOI: 10.1080/03610926.2018.1498895
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    Cited by:

    1. Rafael Weißbach & Dominik Wied, 2022. "Truncating the exponential with a uniform distribution," Statistical Papers, Springer, vol. 63(4), pages 1247-1270, August.

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