A novel bootstrap goodness-of-fit test for normal linear regression models

Scott H. Koeneman () and Joseph E. Cavanaugh ()
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Scott H. Koeneman: Thomas Jefferson University
Joseph E. Cavanaugh: The University of Iowa

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 3, No 3, 443-461

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 DBModelSelect R package, available freely via the Comprehensive R Archive Network.

Keywords: Information criteria; Information matrix test; Normal distribution; Resampling; Robust variance (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10182-024-00517-y

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