메뉴 건너뛰기




Volumn 4, Issue 2, 2010, Pages 764-790

Transposable regularized covariance models with an application to missing data imputation

Author keywords

Covariance estimation; EM algorithm; Imputation; Matrix variate normal; Transposable data

Indexed keywords


EID: 79954998593     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/09-AOAS314     Document Type: Article
Times cited : (105)

References (26)
  • 2
    • 36849079891 scopus 로고    scopus 로고
    • Modeling relationships at multiple scales to imporve accuracy of large recommender systems
    • In, San Jose
    • Bell, R. M., Koren, Y. and Volinsky, C. (2007). Modeling relationships at multiple scales to imporve accuracy of large recommender systems. In Proceedings of KDD Cup and Workshop 95-104. San Jose.
    • (2007) Proceedings of KDD Cup and Workshop , pp. 95-104
    • Bell, R.M.1    Koren, Y.2    Volinsky, C.3
  • 4
    • 0037469122 scopus 로고    scopus 로고
    • Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
    • Biernacki, C., Celeux, G. and Govaert, G. (2003). Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Comput. Statist. Data Anal. 41 561-575.
    • (2003) Comput. Statist. Data Anal , vol.41 , pp. 561-575
    • Biernacki, C.1    Celeux, G.2    Govaert, G.3
  • 6
    • 71049116435 scopus 로고    scopus 로고
    • Exact matrix completion via convex optimization
    • Candes, E. J. and Recht, B. (2009). Exact matrix completion via convex optimization. Found. Comput. Math. 9 717-772.
    • (2009) Found. Comput. Math , vol.9 , pp. 717-772
    • Candes, E.J.1    Recht, B.2
  • 7
    • 0030376179 scopus 로고    scopus 로고
    • Stochastic versions of the EM algorithm: An experimental study in the mixture case
    • Celeux, G., Chauveau, D. and Diebolt, J. (1996). Stochastic versions of the EM algorithm: An experimental study in the mixture case. J. Stat. Comput. Simul. 55 287-314.
    • (1996) J. Stat. Comput. Simul , vol.55 , pp. 287-314
    • Celeux, G.1    Chauveau, D.2    Diebolt, J.3
  • 8
    • 0033236298 scopus 로고    scopus 로고
    • The MLE algorithm for the matrix normal distribution
    • Dutilleul, P. (1999). The MLE algorithm for the matrix normal distribution. J. Stat. Comput. Simul. 64 105-123.
    • (1999) J. Stat. Comput. Simul , vol.64 , pp. 105-123
    • Dutilleul, P.1
  • 9
    • 77949509397 scopus 로고    scopus 로고
    • Are a set of microarrays independent of each other?
    • Efron, B. (2009). Are a set of microarrays independent of each other? Ann. Appl. Statist. 3 922-942.
    • (2009) Ann. Appl. Statist , vol.3 , pp. 922-942
    • Efron, B.1
  • 11
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via penalized likelihood and its oracle properties
    • Fan, J. and Li, R. (2001). Variable selection via penalized likelihood and its oracle properties. J. Amer. Stat. Assoc. 96 1348-1360.
    • (2001) J. Amer. Stat. Assoc , vol.96 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 12
    • 45849134070 scopus 로고    scopus 로고
    • Sparse inverse covariance estimation with the lasso
    • Friedman, J., Hastie, T. and Tibshirani, R. (2007). Sparse inverse covariance estimation with the lasso. Biostatistics 9 432-441.
    • (2007) Biostatistics , vol.9 , pp. 432-441
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 13
    • 0000357775 scopus 로고
    • On use of the EM algorithm for penalized likelihood estimation
    • Green, P. J. (1990). On use of the EM algorithm for penalized likelihood estimation. J. Roy. Statist. Soc. Ser. B 52 443-452.
    • (1990) J. Roy. Statist. Soc. Ser. B , vol.52 , pp. 443-452
    • Green, P.J.1
  • 15
    • 13444304426 scopus 로고    scopus 로고
    • Missing value estimation for DNA microarray gene expression data: Local least squares imputation
    • Kim, H., Golub, G. and Park, H. (2005). Missing value estimation for DNA microarray gene expression data: Local least squares imputation. Bioinformatics 21 187-198.
    • (2005) Bioinformatics , vol.21 , pp. 187-198
    • Kim, H.1    Golub, G.2    Park, H.3
  • 16
    • 33847350805 scopus 로고    scopus 로고
    • Component selection and smoothing in multivariate nonparametric regression
    • Lin, Y. and Zhang, H. H. (2006). Component selection and smoothing in multivariate nonparametric regression. Ann. Statist. 34 2272-2297.
    • (2006) Ann. Statist , vol.34 , pp. 2272-2297
    • Lin, Y.1    Zhang, H.H.2
  • 18
    • 0000251971 scopus 로고
    • Maximum likelihood estimation via the ECM algorithm: A general framework
    • Meng, X.-L. and Rubin, D. (1993). Maximum likelihood estimation via the ECM algorithm: A general framework. Biometrika 80 267-278.
    • (1993) Biometrika , vol.80 , pp. 267-278
    • Meng, X.-L.1    Rubin, D.2
  • 19
    • 62349119614 scopus 로고    scopus 로고
    • Sparse permutation invariant covariance estimation
    • Rothman, A. J., Bickel, P. J., Levina, E. and Zhu, J. (2008). Sparse permutation invariant covariance estimation. Electron. J. Stat. 2 494-515.
    • (2008) Electron. J. Stat , vol.2 , pp. 494-515
    • Rothman, A.J.1    Bickel, P.J.2    Levina, E.3    Zhu, J.4
  • 20
    • 0030539070 scopus 로고    scopus 로고
    • Multiple imputation after 18+ years
    • Rubin, D. B. (1996). Multiple imputation after 18+ years. J. Amer. Statist. Assoc. 91 473-489.
    • (1996) J. Amer. Statist. Assoc , vol.91 , pp. 473-489
    • Rubin, D.B.1
  • 23
    • 66849143711 scopus 로고    scopus 로고
    • Covariance-regularized regression and classification for high-dimensional problems
    • Witten, D. M. and Tibshirani, R. (2009). Covariance-regularized regression and classification for high-dimensional problems. J. R. Stat. Soc. Ser. B Stat. Methodol. 71 615-636.
    • (2009) J. R. Stat. Soc. Ser. B Stat. Methodol , vol.71 , pp. 615-636
    • Witten, D.M.1    Tibshirani, R.2
  • 26
    • 84868967525 scopus 로고    scopus 로고
    • Gene expression profiling predicts survival in conventional renal cell carcinoma
    • Zhao, H., Tibshirani, R. and Brooks, J. (2005). Gene expression profiling predicts survival in conventional renal cell carcinoma. PLOS Medicine 3511-533.
    • (2005) PLOS Medicine , pp. 3511-3533
    • Zhao, H.1    Tibshirani, R.2    Brooks, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.