Conditional quantile estimation with truncated, censored and dependent data

Abstract

This paper deals with the conditional quantile estimation based on left-truncated and right-censored data. Assuming that the observations with multivariate covariates form a stationary α-mixing sequence, the authors derive the strong convergence with rate, strong representation as well as asymptotic normality of the conditional quantile estimator. Also, a Berry-Esseen-type bound for the estimator is established. In addition, the finite sample behavior of the estimator is investigated via simulations.

Keywords

Berry-Esseen-type bound / Conditional quantile estimator / Strong representation / Truncated and censored data / α-mixing

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