Component-wise dimension reduction
Principal components methods and factor analysis are popular tools for the dimension-reduction problem. These techniques can be used to obtain a smaller number of new variables. However, the new variables may include all or most of the original variables. In this study, two methods are given which w...
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Published in | Journal of statistical planning and inference Vol. 114; no. 1; pp. 81 - 93 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Lausanne
Elsevier B.V
01.06.2003
New York,NY Elsevier Science Amsterdam |
Subjects | |
Online Access | Get full text |
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Summary: | Principal components methods and factor analysis are popular tools for the dimension-reduction problem. These techniques can be used to obtain a smaller number of new variables. However, the new variables may include all or most of the original variables. In this study, two methods are given which will select the most informative subset of variables from the variables which are directly measured. The different approaches are compared in a concluding example. |
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ISSN: | 0378-3758 1873-1171 |
DOI: | 10.1016/S0378-3758(02)00464-0 |