Asymptotic normality of kernel density function estimator from continuous time stationary and dependent processes

DOI10.1016/j.spl.2018.09.011zbMath1407.60042OpenAlexW2895332543MaRDI QIDQ1726785

Djamal Louani, Naâmane Laïb

Publication date: 20 February 2019

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

Full work available at URL: https://doi.org/10.1016/j.spl.2018.09.011

zbMATH Keywords

samplingasymptotic normalitykernel estimatordependent datacontinuous timeconfidence bands

Mathematics Subject Classification ID

Density estimation (62G07) Hypothesis testing in multivariate analysis (62H15) Large deviations (60F10) Asymptotic properties of parametric tests (62F05)

Related Items (3)

Asymptotic normality of residual density estimator in stationary and explosive autoregressive models ⋮ Optimal asymptotic MSE of kernel regression estimate for continuous time processes with missing at random response ⋮ On the rates of asymptotic normality for Bernstein polynomial estimators in a triangular array

Cites Work

• Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties
• Asymptotic normality of the kernel estimate under dependence conditions: Application to hazard rate
• Nonparametric statistics for stochastic processes. Estimation and prediction.
• Statistical inference for ergodic diffusion processes.
• Consistency results for the kernel density estimate on continuous time stationary and dependent data
• Generalised kernel smoothing for non-negative stationary ergodic processes
• Probability density estimation from sampled data
• Subadditive mean ergodic theorems
• Optimal asymptotic quadratic error of nonparametric regression function estimates for a continuous-time process from sampled-data
• Optimal sampling for density estimation in continuous time

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