A robust test for homoscedasticity in nonparametric regression

DOI10.1080/10485250903388894zbMath1327.62283OpenAlexW2114777509MaRDI QIDQ3589226

Mareen Marchlewski, Dette, Holger

Publication date: 20 September 2010

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

Full work available at URL: http://hdl.handle.net/2003/26484

zbMATH Keywords

Brownian bridgenonparametric regressionrobust test for heteroscedasticityscale check

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Robustness and adaptive procedures (parametric inference) (62F35)

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Robust testing for superiority between two regression curves ⋮ Tests for the equality of conditional variance functions in nonparametric regression ⋮ Empirical smoothing lack-of-fit tests for variance function ⋮ Bandwidth choice for robust nonparametric scale function estimation ⋮ Statistical tests in the partially linear additive regression models ⋮ A nonparametric hypothesis test for heteroscedasticity ⋮ Test for heteroscedasticity in partially linear regression models ⋮ A model specification test for the variance function in nonparametric regression

Cites Work

• A Simple Test for Heteroscedasticity and Random Coefficient Variation
• Diagnostics for heteroscedasticity in regression
• Testing for constant variance in a linear model
• A Test for Heteroscedasticity Based on Ordinary Least Squares Residuals
• Robust regression function estimation
• A simple test for the parametric form of the variance function in nonparametric regression
• A consistent test for heteroscedasticity in nonparametric regression based on the kernel method
• Cross-validation in nonparametric regression with outliers
• Testing heteroscedasticity in partially linear regression models
• Robust Tests for Heteroscedasticity Based on Regression Quantiles
• Testing Heteroscedasticity In Nonparametric Regression
• Testing homoscedasticity in nonparametric regression
• Designs for Regression Problems with Correlated Errors
• Designs for Regression Problems With Correlated Errors: Many Parameters

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