Testing linearity of regression models with dependent errors by kernel based methods

DOI10.1007/BF02595743zbMath1107.62324OpenAlexW1975284453MaRDI QIDQ5936980

Stefanie Biedermann, Dette, Holger

Publication date: 12 July 2001

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02595743

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

Related Items (10)

SPECIFICATION TESTING IN NONLINEAR TIME SERIES WITH LONG-RANGE DEPENDENCE ⋮ An updated review of goodness-of-fit tests for regression models ⋮ Nonparametric testing for the specification of spatial trend functions ⋮ Testing model assumptions in functional regression models ⋮ SEMI‐PARAMETRIC ANALYSIS OF COVARIANCE UNDER DEPENDENCE CONDITIONS WITHIN EACH GROUP ⋮ A goodness-of-fit test for regression models with spatially correlated errors ⋮ Testing in partial linear regression models with dependent errors ⋮ Testing for Trends in High-Dimensional Time Series ⋮ Measuring the Discrepancy of a Parametric Model via Local Polynomial Smoothing ⋮ Nonparametric analysis of covariance.

Cites Work

• Unnamed Item
• A consistent test of functional form via nonparametric estimation techniques
• Central limit theorems for sequences with \(m(n)\)-dependent main part
• Testing goodness-of-fit in regression via order selection criteria
• Comparing nonparametric versus parametric regression fits
• Testing linear regression models using non-parametric regression estimators when errors are non-independent
• A consistent test for the functional form of a regression based on a difference of variance estimators
• Validation of linear regression models
• Goodness-of-fit test for linear models based on local polynomials
• Bootstrap test of goodness of fit to a linear model when errors are correlated
• On variance estimation in nonparametric regression
• Bootstrap Approximations in Model Checks for Regression
• Test for the choice of approximative models in nonlinear regression when the variance is unknown
• Designs for Regression Problems with Correlated Errors III

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