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Volumn 1, Issue , 2012, Pages 551-558

Estimating sparse precision matrices from data with missing values

Author keywords

[No Author keywords available]

Indexed keywords

EM ALGORITHMS; FROBENIUS NORM; HIGH DIMENSIONS; MISSING VALUES; RATES OF CONVERGENCE; SIMULATION STUDIES; SPECTRAL NORMS; STANDARD TOOLS; TWO-STEP PROCEDURE;

EID: 84867124870     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (34)

References (21)
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  • 3
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    • A constrained 11 minimization approach to sparse precision matrix estimation
    • to appear
    • Cai, T., Liu, W., and Luo, X. A constrained 11 minimization approach to sparse precision matrix estimation. Journal of the American Statistical Association, to appear, 2011.
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  • 4
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    • Optimal rates of convergence for covariance matrix estimation
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    • Cai, T.T.1    Zhang, C.H.2    Zhou, H.H.3
  • 5
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    • Sparse inverse covariance estimation with the graphical lasso
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    • The analysis of incomplete data
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    • Hartley, H.O.1    Hocking, R.R.2
  • 7
    • 0000408215 scopus 로고
    • Estimation of parameters in the multivariate normal distribution with missing observations
    • Hocking, R. R. and Smith, Wm. B. Estimation of parameters in the multivariate normal distribution with missing observations. Journal of the American Statistical Association, 63(321):pp. 159-173, 1968.
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  • 9
    • 84936853890 scopus 로고
    • A test of missing completely at random for multivariate data with missing values
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.