### Abstract

Data-driven lack-of-fit tests are derived for parametric regression models using fit comparison statistics that are based on nonparametric linear smoothers. The tests are applicable to settings where the usual bandwidth/smoothing parameter asymptotics apply to the null model, which includes testing for nonlinear models and some linear models. Large sample distribution theory is established for tests constructed from both kernel and series type estimators. Both types of smoothers are shown to give consistent tests that are asymptotically normal under the null model after appropriate centering and scaling. However, the projection nature of series smoothers results in a simplified scaling factor that produces computational savings for the associated tests.

Original language | English (US) |
---|---|

Pages (from-to) | 135-152 |

Number of pages | 18 |

Journal | Statistica Sinica |

Volume | 15 |

Issue number | 1 |

State | Published - Jan 2005 |

Externally published | Yes |

### Fingerprint

### Keywords

- Bandwidth selection
- Fit comparison test
- Kernel smoother
- Least squares
- Series smoother

### ASJC Scopus subject areas

- Mathematics(all)
- Statistics and Probability

### Cite this

*Statistica Sinica*,

*15*(1), 135-152.

**Testing lack-of-fit of parametric regression models using nonparametric regression techniques.** / Eubank, Randall L.; Li, Chin-Shang; Wang, Suojin.

Research output: Contribution to journal › Article

*Statistica Sinica*, vol. 15, no. 1, pp. 135-152.

}

TY - JOUR

T1 - Testing lack-of-fit of parametric regression models using nonparametric regression techniques

AU - Eubank, Randall L.

AU - Li, Chin-Shang

AU - Wang, Suojin

PY - 2005/1

Y1 - 2005/1

N2 - Data-driven lack-of-fit tests are derived for parametric regression models using fit comparison statistics that are based on nonparametric linear smoothers. The tests are applicable to settings where the usual bandwidth/smoothing parameter asymptotics apply to the null model, which includes testing for nonlinear models and some linear models. Large sample distribution theory is established for tests constructed from both kernel and series type estimators. Both types of smoothers are shown to give consistent tests that are asymptotically normal under the null model after appropriate centering and scaling. However, the projection nature of series smoothers results in a simplified scaling factor that produces computational savings for the associated tests.

AB - Data-driven lack-of-fit tests are derived for parametric regression models using fit comparison statistics that are based on nonparametric linear smoothers. The tests are applicable to settings where the usual bandwidth/smoothing parameter asymptotics apply to the null model, which includes testing for nonlinear models and some linear models. Large sample distribution theory is established for tests constructed from both kernel and series type estimators. Both types of smoothers are shown to give consistent tests that are asymptotically normal under the null model after appropriate centering and scaling. However, the projection nature of series smoothers results in a simplified scaling factor that produces computational savings for the associated tests.

KW - Bandwidth selection

KW - Fit comparison test

KW - Kernel smoother

KW - Least squares

KW - Series smoother

UR - http://www.scopus.com/inward/record.url?scp=17244379745&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=17244379745&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:17244379745

VL - 15

SP - 135

EP - 152

JO - Statistica Sinica

JF - Statistica Sinica

SN - 1017-0405

IS - 1

ER -