Kernel density estimation for linear processes: Asymptotic normality and optimal bandwidth derivation
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Publication:1359395
DOI10.1007/BF00050847zbMath0886.62042OpenAlexW1968772356MaRDI QIDQ1359395
Publication date: 5 April 1998
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf00050847
Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Non-Markovian processes: estimation (62M09)
Related Items (18)
Bias reduction by transformed flat-top Fourier series estimator of density on compact support ⋮ Local linear spatial regression ⋮ Root-\(n\) consistency in weighted \(L _{1}\)-spaces for density estimators of invertible linear processes ⋮ Efficient density estimation in an AR(1) model ⋮ Pointwise convergence rates and central limit theorems for kernel density estimators in linear processes ⋮ Stationary bootstrap for kernel density estimators under \(\psi\)-weak dependence ⋮ Asymptotic Distributions of Innovation Density Estimators in Linear Processes ⋮ Kernel density estimation for spatial processes: The \(L_{1}\) theory ⋮ Nonparametric estimation of conditional expectation ⋮ Functional convergence and optimality of plug-in estimators for stationary densities of moving average processes ⋮ Kernel density estimation for linear processes ⋮ B-spline estimation for spatial data ⋮ Uniformly root-\(n\) consistent density estimators for weakly dependent invertible linear proc\-esses ⋮ Nonparametric density estimation for linear processes with infinite variance ⋮ Reverse chaos may not be a curseexamples of stationary reverse chaotic sequences whose density can be estimated with optimal i.i.d. rate ⋮ Adaptive bandwidth choice ⋮ Estimators in step regression models ⋮ Donsker results for the empirical process indexed by functions of locally bounded variation and applications to the smoothed empirical process
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