Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes

DOI10.1006/jmva.1993.1003zbMath0783.62065OpenAlexW1966318557MaRDI QIDQ1209605

Elias Masry

Publication date: 16 May 1993

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1993.1003

zbMATH Keywords

asymptotic normalitystrong mixing processeskernel-type multivariate density estimatorsmixing stationary processesmultivariate density deconvolution problemtail of the noise characteristic function

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Non-Markovian processes: estimation (62M09)

Related Items (34)

Estimating multivariate density and its derivatives for mixed measurement error data ⋮ Nonparametric regression with errors in variables and applications ⋮ Deconvolving a density from contaminated dependent observations ⋮ Multivariate probability density estimation by wavelet methods: Strong consistency and rates for stationary time series ⋮ Kernel regression estimation with errors-in-variables for random fields ⋮ Parameter estimation for diffusion process from perturbed discrete observations ⋮ Density deconvolution with associated stationary data. ⋮ Nonparametric deconvolution problem for dependent sequences ⋮ Invariance principles for deconvolving kernel density estimation for stationary sequences of random variables ⋮ Nonparametric specification tests for stochastic volatility models based on volatility density ⋮ Optimal bandwidth selection for multivariate kernel deconvolution density estimation ⋮ Nonparametric estimation in time series with measurement errors ⋮ Nonparametric estimation in econometrics ⋮ Nonparametric volatility density estimation for discrete time models ⋮ Nonparametric density estimation from data with a mixture of Berkson and classical errors ⋮ Data-driven deconvolution recursive kernel density estimators defined by stochastic approximation method ⋮ Nonparametric regression for dependent data in the errors-in-variables problem ⋮ Asymptotic normality for wavelet deconvolution density estimators ⋮ Finite sample penalization in adaptive density deconvolution ⋮ Density estimation with heteroscedastic error ⋮ Bivariate kernel deconvolution with panel data ⋮ A review of uncertainty quantification for density estimation ⋮ A Berry-Esseen type bound in kernel density estimation for strong mixing censored samples ⋮ Asymptotic Normality of Kernel-Type Deconvolution Estimators ⋮ Simple kernel estimators for certain nonparametric deconvolution problems ⋮ Parametrically Assisted Nonparametric Estimation of a Density in the Deconvolution Problem ⋮ Nonparametric inference on Lévy measures of compound Poisson-driven Ornstein-Uhlenbeck processes under macroscopic discrete observations ⋮ Asymptotic normality for \(L_1\) norm kernel estimator of conditional median under \(\alpha\)-mixing dependence ⋮ Multivariate partially linear regression in the presence of measurement error ⋮ Multivariate regression estimation with errors-in-variables for stationary processes ⋮ Density estimation for mixed Euclidean and non-Euclidean data in the presence of measurement error ⋮ Least squares cross-validation for the kernel deconvolution density estimator ⋮ Deconvolving multidimensional density from partially contaminated observations ⋮ Consistency and asymptotic normality for a nonparametric prediction under measurement errors

This page was built for publication: Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes