A new approach for selecting the number of factors
In factor analysis, it is critical to determine the number of factors. A new approach to select the number of factors based on unbiased risk estimation is introduced. This approach utilizes a concept, called generalized degrees of freedom (GDF), originally proposed for model selection in regression. A data perturbation technique is applied for estimating GDF. Simulation experiments show that the proposed method performs better than some commonly used methods.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
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.:
- Louis Guttman, 1954. "Some necessary conditions for common-factor analysis," Psychometrika, Springer, vol. 19(2), pages 149-161, June.
- Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer, vol. 52(3), pages 317-332, September.
- Wim Krijnen, 2006. "Convergence of Estimates of Unique Variances in Factor Analysis, Based on the Inverse Sample Covariance Matrix," Psychometrika, Springer, vol. 71(1), pages 193-199, March.
- Dhrymes, Phoebus J & Friend, Irwin & Gultekin, N Bulent, 1984. " A Critical Reexamination of the Empirical Evidence on the Arbitrage Pricing Theory," Journal of Finance, American Finance Association, vol. 39(2), pages 323-46, June.
- Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer, vol. 23(3), pages 187-200, September.
- Huang, Hsin-Cheng & Chen, Chun-Shu, 2007. "Optimal Geostatistical Model Selection," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1009-1024, September.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:2990-2998. 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 you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.