Document Detail


Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.
MedLine Citation:
PMID:  25095276     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.
Authors:
Huiwen Wang; Meiling Chen; Xiaojun Shi; Nan Li
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-29
Journal Detail:
Title:  IEEE transactions on cybernetics     Volume:  -     ISSN:  2168-2275     ISO Abbreviation:  IEEE Trans Cybern     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-8-5     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101609393     Medline TA:  IEEE Trans Cybern     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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