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Volumn 31, Issue , 2013, Pages 48-56

Sparse principal component analysis for high dimensional multivariate time series

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COVARIANCE MATRIX; FINANCIAL DATA PROCESSING; MEDICAL IMAGING; MULTIVARIANT ANALYSIS; TIME SERIES; TIME SERIES ANALYSIS;

EID: 84954233512     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (9)

References (26)
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    • Investigating directed cor- tical interactions in time-resolved fmri data using vector autoregressive modeling and granger causal- ity mapping
    • Goebel, R., Roebroeck, A., Kim, D. and Formisano, E. (2003). Investigating directed cor- tical interactions in time-resolved fmri data using vector autoregressive modeling and granger causal- ity mapping. Magnetic resonance imaging 21 1251-1261.
    • (2003) Magnetic resonance imaging , vol.21 , pp. 1251-1261
    • Goebel, R.1    Roebroeck, A.2    Kim, D.3    Formisano, E.4
  • 8
    • 2342617542 scopus 로고    scopus 로고
    • Multivariate tests for autocor- relation in the stable and unstable var models
    • Hatemi-J, A. (2004). Multivariate tests for autocor- relation in the stable and unstable var models. Eco- nomic Modelling 21 661-683.
    • (2004) Eco- nomic Modelling , vol.21 , pp. 661-683
    • Hatemi-J, A.1
  • 9
    • 66549088006 scopus 로고    scopus 로고
    • On consistency and sparsity for principal components analysis in high dimensions
    • Johnstone, I. and Lu, A. (2009). On consistency and sparsity for principal components analysis in high dimensions. Journal of the American Statis- tical Association 104 682-693.
    • (2009) Journal of the American Statis- tical Association , vol.104 , pp. 682-693
    • Johnstone, I.1    Lu, A.2
  • 15
    • 14244259417 scopus 로고    scopus 로고
    • Mapping directed inuence over the brain using granger causality and fmri
    • Roebroeck, A., Formisano, E., Goebel, R. et al. (2005). Mapping directed inuence over the brain using granger causality and fmri. Neuroimage 25 230-242.
    • (2005) Neuroimage , vol.25 , pp. 230-242
    • Roebroeck, A.1    Formisano, E.2    Goebel, R.3
  • 16
    • 43049086717 scopus 로고    scopus 로고
    • Sparse principal component analysis via regularized low rank matrix approximation
    • Shen, H. and Huang, J. (2008). Sparse principal component analysis via regularized low rank matrix approximation. Journal of multivariate analysis 99 1015-1034.
    • (2008) Journal of multivariate analysis , vol.99 , pp. 1015-1034
    • Shen, H.1    Huang, J.2
  • 17
    • 0000997472 scopus 로고
    • Macroeconomics and reality
    • Sims, C. A. (1980). Macroeconomics and reality. Econometrica 48 1-48. URL http://ideas.repec.org/a/ecm/emetrp/v48y1980i1p1-48.html
    • (1980) Econometrica , vol.48 , pp. 1-48
    • Sims, C.A.1
  • 21
    • 70149096300 scopus 로고    scopus 로고
    • A penalized matrix decomposition, with applica- tions to sparse principal components and canonical correlation analysis
    • Witten, D., Tibshirani, R. and Hastie, T. (2009). A penalized matrix decomposition, with applica- tions to sparse principal components and canonical correlation analysis. Biostatistics 10 515-534.
    • (2009) Biostatistics , vol.10 , pp. 515-534
    • Witten, D.1    Tibshirani, R.2    Hastie, T.3
  • 22
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    • High dimensional inverse covari- ance matrix estimation via linear programming
    • Yuan, M. (2010). High dimensional inverse covari- ance matrix estimation via linear programming. The Journal of Machine Learning Research 99 2261-2286.
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    • Yuan, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.