Bandwidth selection in nonparametric estimator of density derivative by smoothed cross-validation method

DOI10.1134/S0005117910020050OpenAlexW2129222763MaRDI QIDQ1956877

A. V. Dobrovidov, I. M. Ruds'ko

Publication date: 24 September 2010

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117910020050

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (4)

Data-driven density derivative estimation, with applications to nonparametric clustering and bump hunting ⋮ Nonparametric estimation of volatility and its parametric analogs ⋮ Automatic methods of useful signals extraction from noise background under conditions of nonparametric uncertainty ⋮ Uniform almost sure convergence and asymptotic distribution of the wavelet-based estimators of partial derivatives of multivariate density function under weak dependence

Uses Software

• pyuvdata

Cites Work

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• Optimal adaptive control algorithms
• Smoothed cross-validation
• A simple root \(n\) bandwidth selector
• Exact mean integrated squared error
• Nonparametric regression with correlated errors.
• Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation
• Bandwidth Choice for Average Derivative Estimation
• Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation

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