Teaching: not teaching in Fall '13; Stats 503 in Winter 2014
- statistical inference for networks and graphs
- high-dimensional data: large p small n problems, covariance estimation, graphical models, regularization, dimension
- classification and machine learning
- applications of statistics to computer vision,
Raman spectroscopy, and remote sensing
- Elected member of ISI, 2011
- ASA Junior Noether Award, 2010
- Erich Lehmann Dissertation Award, UC Berkeley, 2002
- Annals of Statistics
- Electronic Journal of Statistics
- Journal of Computational and Graphical Statistics
- Statistica Sinica
- J. Cheng, E. Levina, and J. Zhu (2013). High-dimensional mixed graphical models. Submitted.
- J. Cheng, E. Levina, P. Wang, and J. Zhu (2012). Sparse Ising models with covariates. Submitted.
- J. Guo, E. Levina, G. Michailidis, and J. Zhu (2012). Graphical models for ordinal data. Submitted.
- J. Guo, E. Levina, G. Michailidis, and J. Zhu (2011). Estimating
heterogeneous graphical models for discrete data with an application to
roll call voting. Submitted.
- J. Guo, E. Levina, G. Michailidis, and J. Zhu (2011). Asymptotic properties of the joint neighborhood selection method for estimating categorical Markov networks. Submitted.
- E. Levina, and R. Vershynin (2011). Partial estimation of covariance matrices. Probability Theory and Related Fields, DOI: 10.1007/s00440-011-0349-4.
- J.Guo, E. Levina, G. Michailidis, and J. Zhu (2011). Joint estimation of multiple graphical models. Biometrika,
98(1): 1--15. (A winner of the ASA Statistical Learning and Data Mining
Section 2010 Student Paper competition and 2010 Informs Best Student
Paper Award, 1st place).
- A. J. Rothman, E. Levina, and J. Zhu (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics, 19(4): 947--962.
- A. J. Rothman, E. Levina, and J. Zhu (2010). A new approach to Cholesky-based estimation of high-dimensional covariance matrices. Biometrika, 97(3):539--550.
- J.Guo, G. James, E. Levina, G. Michailidis, and J. Zhu (2010). Principal component analysis with sparse fused loadings. Journal of Computational and Graphical Statistics, 19(4): 930--946.
- J.Guo, E. Levina, G. Michailidis, and J. Zhu (2010). Pairwise Variable Selection for High-dimensional Model-based Clustering. Biometrics,
66(3):793--804. (A winner of ENAR 2009 and ASA Statistical
Computing and Graphics Sections 2009 Student Paper Competitions).
- A.J. Rothman, E. Levina, and J. Zhu. (2009). Generalized Thresholding of Large Covariance Matrices. Journal of American Statistical Association (Theory and Methods) 104(485):177-186.
- A.S. Wagaman and E. Levina (2009). Discovering Sparse Covariance Structures with the Isomap. Journal of Computational and Graphical Statistics 18(3):551-572.
- A.J. Rothman, P.J. Bickel, E. Levina, and J. Zhu (2008). Sparse Permutation Invariant Covariance Estimation. Electronic Journal of Statistics 2:494-515. (A winner of ASA Statistical Computing and Graphics Sections 2008 Student Paper Competition).
- P.J. Bickel and E. Levina (2008). Covariance Regularization by Thresholding. Annals of Statistics 36(6):2577-2604.
- E. Levina, A.J. Rothman and J. Zhu (2008). Sparse Estimation of Large Covariance Matrices via a Nested Lasso Penalty. Annals of Applied Statistics 2(1):245-263.
- P.J. Bickel and E. Levina (2008). Regularized Estimation of Large Covariance Matrices. Annals of Statistics 36(1):199-227.
- E. Levina, A.S. Wagaman, A.F. Callender, G.S. Mandair, and M.D. Morris (2007). Estimating the number of pure chemical components in a mixture by maximum likelihood. Journal of Chemometrics 21(1-2):24-34.
- E. Levina and P.J. Bickel (2005). Maximum Likelihood Estimation of Intrinsic
Dimension. In Advances in
NIPS 17, Eds. L. K. Saul, Y. Weiss, L. Bottou. Matlab code is available.
- P.J. Bickel and E. Levina (2004). Some theory for Fisher's Linear Discriminant
''naive Bayes'', and some alternatives when there are many more
than observations. Bernoulli
Statistics in Computer Vision
- Zhao, Y., Levina, E. and Zhu, J. (2013). Link prediction for partially observed networks. Submitted.
- Amini, A. A., Chen, A., Bickel, P.J., and Levina, E. (2012). Pseudo-likelihood methods for community detection in large sparse networks. Annals of Statistics, to appear.
- Zhao, Y., Levina, E. and Zhu, J. (2012). Consistency of community detection in networks under degree-corrected stochastic block models. Annals of Statistics, 40(4):2266-2292.
- Bickel, P.J., Chen,
A., and Levina, E. (2011). The method of moments and degree
distributions for network models. Annals of Statistics, 39(5):2280-2301.
- Zhao, Y., Levina, E. and Zhu, J. (2011). Community extraction for social networks. Proceedings of the National Academy of Sciences, 108(18):7321-7326. (A winner of ASA Statistical Computing and Graphics Sections 2011 Student Paper Competition).
- B. Karrer, E. Levina, and M.E.J. Newman (2008). Robustness of Community Structure in Networks. Physical Review E, 77(4):046119.
- N. Katenka, E. Levina, and G. Michailidis
(2013). Tracking multiple targets using binary decisions from wireless
sensor networks. JASA Case Studies and Applications, in press.
- N. Katenka, E. Levina, and G. Michailidis (2008). Robust Target Localization from Binary Decisions in Wireless Sensor Networks.
- N. Katenka, E. Levina, and G. Michailidis (2008). Local Vote Decision Fusion for Target
Detection in Wireless Sensor Networks.
IEEE Transactions on Signal Processing 56(1):329-338.
- Zhang, M., Levina, E., Djurdjanovic, D.,
and Ni, J. (2008). Estimating distributions of surface parameters for
classification purposes. Journal of Manufacturing Science and Engineering, 130(3):031010.
- E. Levina and P.J. Bickel (2006). Texture
Synthesis and Non-parametric Resampling of Random Fields. Annals of Statistics 34(4):1751-1773.
- E. Levina and P.J. Bickel (2001). The Earth Mover's Distance is the Mallows Distance:
Some Insights from Statistics. In Proceedings of ICCV 2001,
Vancouver, Canada, p. 251-256.
supported in part by the NSF (DMS-0505424, DMS-0805798, DMS-01106772,
DMS-1159005), NIH (5-R01-AR-056646-03) and NSA (MSPF-04Y-120).
Any opinions, findings, and conclusions or recommendations expressed in
this material are those of the author(s) and do not necessarily reflect
the views of the National Science Foundation, the National Institutes
of Health, and the National Security Agency.
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