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A Stepwise AIC Method for Variable Selection in Linear Regression
Abstract
In this article, we study stepwise AIC method for variable selection comparing with other stepwise method for variable selection, such as, Partial F, Partial Correlation, and Semi-Partial Correlation in linear regression modeling. Then we show mathematically that the stepwise AIC method and other stepwise methods lead to the same method as Partial F. Hence, there are more reasons to use the stepwise AIC method than the other stepwise methods for variable selection, since the stepwise AIC method is a model selection method that can be easily managed and can be widely extended to more generalized models and applied to non normally distributed data. We also treat problems that always appear in applications, that are validation of selected variables and problem of collinearity.
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Cited by
Conroy-Beam, D., Goetz, C. D., & Buss, D. M. (2016) · Evolution and Human Behavior · doi:10.1016/j.evolhumbehav.2016.04.003 · Cited by 107
Baecker, A., Kim, S., Risch, H. A., Nuckols, T. K., Wu, B. U., Hendifar, A. E., Pandol, S. J., Pisegna, J. R., & Jeon, C. Y. (2019) · PLOS ONE · doi:10.1371/journal.pone.0218580 · Cited by 31
Metadata
- Authors
- Yamashita, T., Yamashita, K., & Kamimura, R.
- Venue
- Communications in Statistics - Theory and Methods
- Publisher
- Informa UK Limited
- Published
- 2007-10-03
- Volume
- 36
- Issue
- 13
- Pages
- 2395-2403
- ISSN
- 0361-0926 (ISSN-L)
- 1532-415X
- OpenAlex
- W2074977053
- Cited by
- 316
- References
- 0
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- closed
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