Testing linear regression models using non-parametric regression estimators when errors are non-independent

DOI10.1016/0167-9473(94)00058-QzbMath0900.62192OpenAlexW1974749959MaRDI QIDQ1350264

Wenceslao González Manteiga, Juan M. Vilar Fernández

Publication date: 27 February 1997

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-9473(94)00058-q

zbMATH Keywords

time serieshypothesis testingregression modelsnon-parametric estimators

Mathematics Subject Classification ID

Density estimation (62G07) Linear regression; mixed models (62J05)

Related Items (11)

An updated review of goodness-of-fit tests for regression models ⋮ Goodness-of-fit tests for multiple regression with circular response ⋮ On detecting non‐monotonic trends in environmental time series: a fusion of local regression and bootstrap ⋮ Testing model assumptions in functional regression models ⋮ A goodness-of-fit test for regression models with spatially correlated errors ⋮ Testing linearity of regression models with dependent errors by kernel based methods ⋮ Resampling for checking linear regression models via non-parametric regression estimation ⋮ Testing in partial linear regression models with dependent errors ⋮ Testing for Trends in High-Dimensional Time Series ⋮ Bootstrap test of goodness of fit to a linear model when errors are correlated ⋮ Bootstrap of minimum distance estimators in regression with correlated disturbances

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