A New Bootstrap Goodness-of-Fit Test for Normal Linear Regression Models

Scott H Koeneman; Joseph E Cavanaugh

doi:10.48550/arxiv.2309.10614

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A New Bootstrap Goodness-of-Fit Test for Normal Linear Regression Models

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A New Bootstrap Goodness-of-Fit Test for Normal Linear Regression Models

Scott H Koeneman and Joseph E Cavanaugh

ArXiv.org

Cornell University

09/19/2023

DOI: 10.48550/arxiv.2309.10614

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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 non-parametric 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.

Details

Title: Subtitle: A New Bootstrap Goodness-of-Fit Test for Normal Linear Regression Models
Creators: Scott H Koeneman
Joseph E Cavanaugh
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2309.10614
ISSN: 2331-8422
Publisher: Cornell University
Language: English
Date posted: 09/19/2023
Academic Unit: Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center; Internal Medicine
Record Identifier: 9984466773102771

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