Nonparametric adaptive estimation for integrated diffusions

DOI10.1016/j.spa.2008.04.009zbMath1157.62054OpenAlexW2011000414MaRDI QIDQ1009666

Yves Rozenholc, Valentine Genon-Catalot, Fabienne Comte

Publication date: 2 April 2009

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spa.2008.04.009

zbMATH Keywords

model selectiondiffusion processadaptive estimationdiffusion coefficientintegrated processdriftdiscrete observationmean square estimator

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09) Markov processes: estimation; hidden Markov models (62M05)

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Cites Work

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• LAMN property for hidden processes: the case of integrated diffusions
• Nonparametric adaptive estimation for integrated diffusions
• Estimation for diffusion processes from discrete observation
• Risk bounds for model selection via penalization
• Inequalities for absolutely regular sequences: application to density estimation
• Adaptive estimation in diffusion processes.
• Estimators of diffusions with randomly spaced discrete observations: a general theory
• A new algorithm for fixed design regression and denoising
• Density estimation by wavelet thresholding
• Penalized nonparametric mean square estimation of the coefficients of diffusion processes
• Exact simulation of diffusions
• Retrospective exact simulation of diffusion sample paths with applications
• Discrete sampling of an integrated diffusion process and parameter estimation of the diffusion coefficient
• Prediction‐based estimating functions
• Exact and Computationally Efficient Likelihood-Based Estimation for Discretely Observed Diffusion Processes (with Discussion)
• Parameter Estimation for a Discretely Observed Integrated Diffusion Process
• Estimation of an Ergodic Diffusion from Discrete Observations
• On the inverse of the first hitting time problem for bidimensional processes
• Likelihood Inference for Discretely Observed Nonlinear Diffusions
• Model selection for (auto-)regression with dependent data
• Inference for Observations of Integrated Diffusion Processes
• Penalized Projection Estimator for Volatility Density
• Stochastic volatility models as hidden Markov models and statistical applications