On the choice of smoothing parameters for semirecursive nonparametric hazard estimators

DOI10.1080/15598608.2016.1214853zbMath1420.62148OpenAlexW2495234132WikidataQ57519953 ScholiaQ57519953MaRDI QIDQ2323198

Yousri Slaoui

Publication date: 30 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15598608.2016.1214853

zbMATH Keywords

smoothingcurve fittingstochastic approximation algorithmnonparametric hazard estimation

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Nonparametric estimation (62G05) Stochastic approximation (62L20)

Related Items (7)

Automatic bandwidth selection for recursive kernel density estimators with length-biased data ⋮ The stochastic approximation method for recursive kernel estimation of the conditional extreme value index ⋮ Recursive kernel regression estimation under α – mixing data ⋮ Optimal bandwidth selection for recursive Gumbel kernel density estimators ⋮ Data-driven bandwidth selection for recursive kernel density estimators under double truncation ⋮ The rate of complete consistency for recursive probability density estimator under strong mixing samples ⋮ Recursive kernel density estimation and optimal bandwidth selection under \(\alpha\): mixing data

Uses Software

• bshazard

Cites Work

• Local linear hazard rate estimation and bandwidth selection
• Practical bandwidth selection in deconvolution kernel density estimation
• The stochastic approximation method for the estimation of a multivariate probability density
• Optimal bandwidth selection for semi-recursive kernel regression estimators
• Bandwidth selection for kernel distribution function estimation
• Bandwidth selection for recursive kernel density estimators defined by stochastic approximation method
• The stochastic approximation method for estimation of a distribution function
• A companion for the Kiefer-Wolfowitz-Blum stochastic approximation algorithm
• A unified theory of regularly varying sequences
• Hazard Rate Estimation under Random Censoring with Varying Kernels and Bandwidths
• Large and moderate deviation principles for recursive kernel density estimators defined by stochastic approximation method
• Estimation of Jumps, Reliability and Hazard Rate
• Regularly Varying Sequences
• Plug‐in bandwidth selector for recursive kernel regression estimators defined by stochastic approximation method
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