Abstract
AbstractWe focus on the problem of selection of a subset of the variables so as to preserve the multivariate data structure that a principal‐components analysis of the initial variables would reveal. We propose a new method based on some adapted Gaussian graphical models. This method is then compared with those developed by Bonifas et al. (1984) and Krzanowski (1987a, b). It appears that the criteria for all methods consider the same correlation submatrices and often lead to similar results. The proposed approach offers some guidance as to the number of variables to be selected. In particular, Akaike's information criterion is used.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.