Electronic Journal of Statistics

Nonparametric conditional density estimation for censored data based on a recursive kernel

Salah Khardani and Sihem Semmar

Full-text: Open access

Abstract

Consider a regression model in which the response is subject to random right censoring. The main goal of this paper concerns the kernel estimation of the conditional density function in the case of censored interest variable. We employ a recursive version of the Nadaraya-Watson estimator in this context. The uniform strong consistency of the recursive kernel conditional density estimator is derived. Also, we prove the asymptotic normality of this estimator.

Article information

Source
Electron. J. Statist., Volume 8, Number 2 (2014), 2541-2556.

Dates
First available in Project Euclid: 9 December 2014

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1418134263

Digital Object Identifier
doi:10.1214/14-EJS960

Mathematical Reviews number (MathSciNet)
MR3285875

Zentralblatt MATH identifier
1309.62066

Subjects
Primary: 62G05: Estimation 62G07: Density estimation 62G08: Nonparametric regression 62G20: Asymptotic properties 62H12: Estimation

Keywords
Asymptotic normality censored data conditional density kernel estimator recursive estimation Kaplan–Meier estimator uniform almost sure convergence

Citation

Khardani, Salah; Semmar, Sihem. Nonparametric conditional density estimation for censored data based on a recursive kernel. Electron. J. Statist. 8 (2014), no. 2, 2541--2556. doi:10.1214/14-EJS960. https://projecteuclid.org/euclid.ejs/1418134263


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