Semi-recursive kernel conditional density estimators under random censorship and dependent data

DOI10.1080/03610926.2020.1764038MaRDI QIDQ6587713

Sihem Semmar, Salah Khardani, Ali Laksaci

Publication date: 14 August 2024

Published in: Communications in Statistics. Theory and Methods (Search for Journal in Brave)

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Cites Work

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• Prediction from randomly right censored data
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• Stochastic algorithms
• Asymptotic theory of weakly dependent stochastic processes
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• Uniform rate of strong consistency for a smooth kernel estimator of the conditional mode for censored time series
• Recursive kernel density estimation and optimal bandwidth selection under \(\alpha\): mixing data
• Nonparametric relative regression under random censorship model
• Asymptotic normality of recursive estimators under strong mixing conditions
• Some asymptotic properties for a smooth kernel estimator of the conditional mode under random censorship
• Non linear parametric mode regression
• Strong Laws of Large Numbers and Nonparametric Estimation
• Nonparametric Estimation from Incomplete Observations
• The L/sub 1/ and L/sub 2/ strong consistency of recursive kernel density estimation from dependent samples
• Recursive probability density estimation for weakly dependent stationary processes
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
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• Nonlinear autoregressive processes
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• Conditional Minimum Volume Predictive Regions for Stochastic Processes
• Nonparametric Estimation and Symmetry Tests for Conditional Density Functions
• Large and moderate deviation principles for recursive kernel density estimators defined by stochastic approximation method
• Asymptotically optimal discriminant functions for pattern classification
• A Note on Permanents
• A Stochastic Approximation Method

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