On bootstrapping the mode in the nonparametric regression model with random design

DOI10.1007/PL00003988zbMath0990.62036MaRDI QIDQ5953823

Klaus Ziegler

Publication date: 29 January 2002

Published in: Metrika (Search for Journal in Brave)

zbMATH Keywords

asymptotic normalitykernel smoothingbandwidth selectionbootstrap central limit theoremrandom designsmoothed bootstrap

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric statistical resampling methods (62G09)

Related Items (8)

On nonparametric kernel estimation of the mode of the regression function in the random design model ⋮ Confidence sets for the maximizers of intensity functions ⋮ Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications ⋮ On the asymptotic normality of kernel regression estimators of the mode in the nonparametric random design model. ⋮ Asymptotic normality of the regression mode in the nonparametric random design model for censored data ⋮ Some asymptotic properties of kernel regression estimators of the mode for stationary and ergodic continuous time processes ⋮ Inference for the mode of a log-concave density ⋮ Asymptotics for function derivatives estimators based on stationary and ergodic discrete time processes

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