Abstract
We propose a natural test of fit of a parametric regression model. The test is based on a comparison of a nonparametric kernel estimate of a regression function with its least-squares parametric estimate. Under the null hypothesis we derive approximations to the probability distribution functions of the test statistic. The approximations are exact with a power rate. Moreover, we prove the consistency of the test.
| Original language | English |
|---|---|
| Pages (from-to) | 66-75 |
| Number of pages | 10 |
| Journal | Journal of Multivariate Analysis |
| Volume | 37 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1991 |
| Externally published | Yes |
Keywords
- least squares method
- maximum deviation distribution
- nonlinear regression
- nonparametric regression
- parametric regression
- test of fit