Asymptotic normality of kernel density function estimator from continuous time stationary and dependent processes
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Publication:1726785
DOI10.1016/j.spl.2018.09.011zbMath1407.60042OpenAlexW2895332543MaRDI QIDQ1726785
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
Density estimation (62G07) Hypothesis testing in multivariate analysis (62H15) Large deviations (60F10) Asymptotic properties of parametric tests (62F05)
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Cites Work
- Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties
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- 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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