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Global Validation of Linear Model Assumptions
Open accessAbstract
An easy-to-implement global procedure for testing the four assumptions of the linear model is proposed. The test can be viewed as a Neyman smooth test and it only relies on the standardized residual vector. If the global procedure indicates a violation of at least one of the assumptions, the components of the global test statistic can be utilized to gain insights into which assumptions have been violated. The procedure can also be used in conjunction with associated deletion statistics to detect unusual observations. Simulation results are presented indicating the sensitivity of the procedure in detecting model violations under a variety of situations, and its performance is compared with three potential competitors, including a procedure based on the Box-Cox power transformation. The procedure is demonstrated by applying it to a new car mileage data set and a water salinity data set that has been used previously to illustrate model diagnostics.
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Cited by
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Brettschneider, J., Toledo, J. B., Van Deerlin, V. M., Elman, L., McCluskey, L., Lee, V. M.-Y., & Trojanowski, J. Q. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039216 · Cited by 268
Gray matter network disruptions and amyloid beta in cognitively normal adults
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Metadata
- Authors
- Peña, E. A., & Slate, E. H.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2006-03
- Volume
- 101
- Issue
- 473
- Pages
- 341-354
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1998258821
- Cited by
- 446
- References
- 0
- FWCI
- 0.92
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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