Rates of convergence and asymptotic normality of kernel estimators for ergodic diffusion processes

DOI10.1080/10485250108832880zbMath0999.62030OpenAlexW2167387949MaRDI QIDQ4551596

Harry van Zanten

Publication date: 3 December 2002

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250108832880

zbMATH Keywords

rate of convergenceasymptotic normalityergodic diffusions

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Markov processes: estimation; hidden Markov models (62M05)

Related Items (6)

Estimation of the invariant density for discretely observed diffusion processes: impact of the sampling and of the asynchronicity ⋮ Flexible Bayesian inference for diffusion processesusing splines ⋮ Parametric estimation from approximate data: non-Gaussian diffusions ⋮ Nonparametric Bayesian inference for ergodic diffusions ⋮ Optimal convergence rates for the invariant density estimation of jump-diffusion processes ⋮ Sharp adaptive estimation of the drift function for ergodic diffusions

Cites Work

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• Density estimation in a continuous-time stationary Markov process
• Can we estimate the density's derivative with suroptimal rate
• Efficient density estimation for ergodic diffusion processes
• Nonparametric Identification for Diffusion Processes
• Asymptotic Statistics
• Joint Asymptotic Distribution of the Estimated Regression Function at a Finite Number of Distinct Points
• On the uniform convergence of the empirical density of an ergodic diffusion

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