## Abstract

This article presents a method for the construction of a simultaneous confidence band for the normal-error multiple linear regression model. The confidence bands considered have their width proportional to the standard error of the estimated regression function, and the predictor variables are allowed to be constrained in intervals. Past articles in this area gave exact bands only for the simple regression model. When there is more than one predictor variable, only conservative bands are proposed in the statistics literature. This article advances this methodology by providing simulation-based confidence bands for regression models with any number of predictor variables. Additionally, a criterion is proposed to assess the sensitivity of a simultaneous confidence band. This criterion is defined to be the probability that a false linear regression model is excluded from the band at least at one point and hence this false linear regression model is correctly declared as a false model by the band. Finally, the article considers and compares several computational algorithms for obtaining the confidence band.

Original language | English (US) |
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Pages (from-to) | 459-484 |

Number of pages | 26 |

Journal | Journal of Computational and Graphical Statistics |

Volume | 14 |

Issue number | 2 |

DOIs | |

State | Published - Jun 2005 |

Externally published | Yes |

## Keywords

- Inequality constraints
- Linear regression
- Polyhedral cone
- Projection
- Quadratic programming
- Statistical simulation

## ASJC Scopus subject areas

- Statistics and Probability
- Discrete Mathematics and Combinatorics
- Statistics, Probability and Uncertainty