A nonparametric test of fit of a linear model
- 1 January 1990
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 19 (1) , 169-179
- https://doi.org/10.1080/03610929008830195
Abstract
In the paper we derive a test of fit of a linear model based on a nonparametic kernel estimator of a regression function. The test is especially useful when no replication is available.Keywords
This publication has 6 references indexed in Scilit:
- Testing the (Parametric) Null Model Hypothesis in (Semiparametric) Partial and Generalized Spline ModelsThe Annals of Statistics, 1988
- On Almost Sure Convergence of Conditional Empirical Distribution FunctionsThe Annals of Probability, 1986
- Testing Linear Regression Function Adequacy without ReplicationThe Annals of Statistics, 1985
- On the convergence rate of maximal deviation distribution for kernel regression estimatesJournal of Multivariate Analysis, 1984
- Testing for lack of fit in regression - a reviewCommunications in Statistics - Theory and Methods, 1984
- Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in RegressionJournal of the Royal Statistical Society Series B: Statistical Methodology, 1978