Kernel estimation of conditional density with truncated, censored and dependent data

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Abstract

In this paper we define a kernel estimator of the conditional density for a left-truncated and right-censored model based on the generalized product-limit estimator of the conditional distributed function. Under the observations with multivariate covariates form a stationary α-mixing sequence, we derive the asymptotic normality as well as a Berry-Esseen type bound for the proposed estimator. Also, the uniform convergence with rates for the estimator is considered. Finite sample behavior of the estimator is investigated via simulations too. © 2013 Elsevier Inc.

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APA

Liang, H. Y., & Liu, A. A. (2013). Kernel estimation of conditional density with truncated, censored and dependent data. Journal of Multivariate Analysis, 120, 40–58. https://doi.org/10.1016/j.jmva.2013.05.009

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