A GIC rule for assessing data transformation in regression
The functional form used in regression may be generalized by the Box-Cox transformation. We adopt the generalized information criterion (GIC)Â approach to determine a need for Box-Cox (J. Roy. Statist. Soc. Ser. B 26 (1964) 211) transformation of the response variable. The utilization of the constructed variable reduces the problem to one of variable selection based on GIC. Our method leads to comparing the partial correlation coefficient between the dependent variable and the constructed variable of an artificial regression, with critical values depending on a penalty parameter. The method is illustrated with simulation examples and several well-known examples from the literature in regression diagnostics.
Volume (Year): 68 (2004)
Issue (Month): 1 (June)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Cho, Kwanho & Yeo, In-Kwon & Johnson, Richard A. & Loh, Wei-Yin, 2001. "Asymptotic theory for Box-Cox transformations in linear models," Statistics & Probability Letters, Elsevier, vol. 51(4), pages 337-343, February.
When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:68:y:2004:i:1:p:105-110. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.