On nonparametric kernel estimation of the mode of the regression function in the random design model

DOI10.1080/10485250215321zbMath1013.62047OpenAlexW2057428202MaRDI QIDQ4806547

Klaus Ziegler

Publication date: 23 June 2003

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250215321

zbMATH Keywords

consistencyasymptotic normalitykernel smoothingNadaraya-Watson estimatorrandom designmodeestimation of derivativesdata-dependent bandwidths

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20)

Related Items

Rates of consistency for nonparametric estimation of the mode in absence of smoothness assumptions, Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications, 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, On Semiparametric Mode Regression Estimation, Assessing extrema of empirical principal component functions, Uniform convergence rate of the kernel regression estimator adaptive to intrinsic dimension in presence of censored data, Asymptotics for function derivatives estimators based on stationary and ergodic discrete time processes, Space partitioning and regression maxima seeking via a mean-shift-inspired algorithm

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