Exact asymptotic \(\mathcal L_1\)-error of a kernel density estimator under censored data.

DOI10.1016/S0167-7152(02)00278-XzbMath1092.62541OpenAlexW2052397794MaRDI QIDQ1871313

Elias Ould Saïd, Mohamed Lemdani

Publication date: 7 May 2003

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(02)00278-x

zbMATH Keywords

Censored dataKernel estimatorStrong approximation\(\mathcal L_1\)-error

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (1)

Convergence Rates of Empirical Bayes Estimation for the Parameter of the Uniform DistributionU(0, θ) Under Random Censorship

Cites Work

• A correction to and improvement of: ``Strong approximations of some biometric estimates under random censorship
• Strong embedding of the estimator of the distribution function under random censorship
• Central limit theorems for \(L_ p\) distances of kernel estimators of densities under random censorship
• The strong law under random censorship
• A large sample study of the life table and product limit estimates under random censorship
• Relative deficiency of the Kaplan-Meier estimator with respect to a smoothed estimator
• A bound on the \(\mathcal{L}_ 1\)-error of a nonparametric density estimator with censored data
• Some bounds for the error of an estimator of the hazard function with censored data
• Nonparametric Estimation from Incomplete Observations
• L₁-Consistency of the kernel density estimators based on randomly right censored data
• Strong approximations of some biometric estimates under random censorship

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