Journal article
A novel bootstrap goodness-of-fit test for normal linear regression models
Advances in statistical analysis : AStA : a journal of the German Statistical Society, Vol.109(3), pp.443-461
09/2025
DOI: 10.1007/s10182-024-00517-y
Abstract
Abstract In this work, the distributional properties of the goodness-of-fit term in likelihood-based information criteria are explored. These properties are then leveraged to construct a novel goodness-of-fit test for normal linear regression models that relies on a nonparametric bootstrap. Several simulation studies are performed to investigate the properties and efficacy of the developed procedure, with these studies demonstrating that the bootstrap test offers distinct advantages as compared to other methods of assessing the goodness-of-fit of a normal linear regression model. Our inferential technique can be employed using the R package, available freely via the Comprehensive R Archive Network.
Details
- Title: Subtitle
- A novel bootstrap goodness-of-fit test for normal linear regression models
- Creators
- Scott H. KoenemanJoseph E. Cavanaugh
- Resource Type
- Journal article
- Publication Details
- Advances in statistical analysis : AStA : a journal of the German Statistical Society, Vol.109(3), pp.443-461
- DOI
- 10.1007/s10182-024-00517-y
- ISSN
- 1863-8171
- eISSN
- 1863-818X
- Publisher
- SPRINGER
- Language
- English
- Electronic publication date
- 11/14/2024
- Date published
- 09/2025
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984749760002771
Metrics
18 Record Views