Strong approximation of density estimators from weakly dependent observations by density estimators from independent observations

DOI10.1214/aos/1024691367zbMath0930.62038OpenAlexW1968066580MaRDI QIDQ1807141

Michael H. Neumann

Publication date: 9 November 1999

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1024691367

zbMATH Keywords

bootstraptime seriesmixingdensity estimationstrong approximationweak dependencesimultaneous confidence bandswhitening by windowing

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09) Strong limit theorems (60F15)

Related Items (23)

Bootstraps for time series ⋮ Confidence sets for nonparametric wavelet regression ⋮ CONDITIONAL STOCHASTIC DOMINANCE TESTS IN DYNAMIC SETTINGS ⋮ Kernel estimation for time series: an asymptotic theory ⋮ Confidence regions for level sets ⋮ On semiparametric inference for periodically modulated density functions ⋮ Estimation of complier expected shortfall treatment effects with a binary instrumental variable ⋮ Oscillations and moduli of continuity of kernel density estimators under dependence ⋮ Gaussian approximation of suprema of empirical processes ⋮ Confidence sets for persistence diagrams ⋮ Smooth estimation of a distribution and density function on a hypercube using Bernstein polynomials for dependent random vectors ⋮ Properties of the nonparametric autoregressive bootstrap ⋮ Simultaneous nonparametric inference of time series ⋮ A review of uncertainty quantification for density estimation ⋮ Regression-type inference in nonparametric autoregression ⋮ Non-parametric Poisson regression from independent and weakly dependent observations by model selection ⋮ Exact asymptotics for estimating the marginal density of discretely observed diffusion proc\-esses ⋮ Adaptive drift estimation for nonparametric diffusion model. ⋮ Multiscale maximum likelihood analysis of a semiparametric model, with applications. ⋮ Weak dependence beyond mixing and asymptotics for nonparametric regression ⋮ On bootstrapping \(L_2\)-type statistics in density testing ⋮ Time-varying nonlinear regression models: nonparametric estimation and model selection ⋮ Confidence bands for multivariate and time dependent inverse regression models

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