Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18481
Title: A GIC rule for assessing data transformation in regression
Authors: Ip, WC
Wong, H 
Wang, SG
Jia, ZZ
Keywords: Box-Cox transformation
Constructed variable
Generalized information criterion
Penalty parameter
Issue Date: 2004
Publisher: Elsevier Science Bv
Source: Statistics and probability letters, 2004, v. 68, no. 1, p. 105-110 How to cite?
Journal: Statistics and Probability Letters 
Abstract: The functional form used in regression may be generalized by the Box-Cox transformation. We adopt the generalized information criterion (GIC) 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.
URI: http://hdl.handle.net/10397/18481
ISSN: 0167-7152
DOI: 10.1016/j.spl.2004.01.019
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Last Week
0
Last month
0
Citations as of Apr 21, 2017

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Apr 20, 2017

Page view(s)

14
Last Week
0
Last month
Checked on Apr 23, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.