Functional convergence and optimality of plug-in estimators for stationary densities of moving average processes

DOI10.3150/bj/1099579161zbMath1058.62072OpenAlexW1968564032MaRDI QIDQ1769788

Anton Schick, Wolfgang Wefelmeyer

Publication date: 30 March 2005

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3150/bj/1099579161

zbMATH Keywords

functional central limit theoremtime seriessemiparametric modelefficient estimatorplug-in estimatorleast dispersed estimator

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05) Functional limit theorems; invariance principles (60F17) (L^p)-limit theorems (60F25)

Related Items

Density estimation for nonlinear parametric models with conditional heteroscedasticity ⋮ Root n consistent and optimal density estimators for moving average processes ⋮ Efficient prediction for linear and nonlinear autoregressive models ⋮ Root-\(n\) consistency in weighted \(L _{1}\)-spaces for density estimators of invertible linear processes ⋮ Estimating the density of a possibly missing response variable in nonlinear regression ⋮ Efficient density estimation in an AR(1) model ⋮ Uniform convergence of convolution estimators for the response density in nonparametric regression ⋮ Rootnconsistent density estimators for sums of independent random variables ⋮ Some developments in semiparametric statistics ⋮ Uniformly root-\(n\) consistent density estimators for weakly dependent invertible linear proc\-esses ⋮ Improved Density Estimators for Invertible Linear Processes ⋮ Optimal plug-in estimators for multivariate distributions with conditionally independent components ⋮ Plug-in estimators for higher-order transition densities in autoregression ⋮ Non Standard Behavior of Density Estimators for Functions of Independent Observations

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