Tests for normality based on density estimators of convolutions

DOI10.1016/J.SPL.2010.10.022zbMath1205.62055OpenAlexW1992820788MaRDI QIDQ625030

Wolfgang Wefelmeyer, Anton Schick, Yishi Wang

Publication date: 11 February 2011

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2010.10.022

zbMATH Keywords

goodness-of-fit testconvolution-type kernel density estimator

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric hypothesis testing (62G10) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20)

Related Items (8)

On a test of normality based on the empirical moment generating function ⋮ Uniform in bandwidth consistency of conditional \(U\)-statistics adaptive to intrinsic dimension in presence of censored data ⋮ On the variable bandwidth kernel estimation of conditional \(U\)-statistics at optimal rates in sup-norm ⋮ Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional U-statistics involving functional data ⋮ Weak-convergence of empirical conditional processes and conditional \(U\)-processes involving functional mixing data ⋮ Central limit theorems for conditional empirical and conditional \(U\)-processes of stationary mixing sequences ⋮ A Correlation Test for Normality Based on the Lévy Characterization ⋮ Uniform consistency and uniform in number of neighbors consistency for nonparametric regression estimates and conditional \(U\)-statistics involving functional data

Uses Software

• KernSmooth

Cites Work

• Unnamed Item
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• On local \(U\)-statistic processes and the estimation of densities of functions of several sample variables
• On the estimation of the marginal density of a moving average process
• Estimating Densities of Functions of Observations
• An alernative test for normality based on normalized spacings
• Rootnconsistent density estimators for sums of independent random variables
• An analysis of variance test for normality (complete samples)
• A test for normality based on the empirical characteristic function

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