University of Southern California

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Yingying Fan
Assistant Professor of Data Sciences and Operations

USC Marshall School of Business
Los Angeles, CA 90089-0808

Phone: 
213-740-9916
fanyingy@marshall.usc.edu
Education: 
PhD, MS, Princeton University; BS, University of Science and Technology of China
Personal Website: 
http://www-bcf.usc.edu/~fanyingy

Overview

Yingying Fan's research interests include high dimensional statistical inference, classification and variable selection, nonparametric statistics, and financial econometrics. Her papers have been published in journals including the Annals of Statistics, Journal of the American Statistical Association, Journal of Econometrics, and Journal of Financial Econometrics. She was Lecturer in the Department of Statistics at Harvard University from 2007-2008. She is the recipient of National Science Foundation Grant (2009-2012, PI), 2010 Zumberge Individual Award from USC's James H. Zumberge Faculty Research and Innovation Fund (2010-2011, PI), and 2010 Dean's Award for Research Excellence. She also serves in the membership committee of the International Chinese Statistical Association (2009-Present).

Research

Optimal classification in sparse Gaussian graphic model • 2013
Asymptotic properties for combined L1 and concave regularization • 2013
Asymptotic equivalence of regularization methods in thresholded parameter space • 2013
Adaptive robust variable selection • 2013
High-dimensional thresholded regression and shrinkage effect • 2013
Tuning parameter selection in high dimensional penalized likelihood • 2013
Discussion of "Large covariance estimation by thresholding principal orthogonal complements" • 2013
Variable selection in linear mixed effects models • 2012
Testing and detecting jumps based on a discretely observed process • 2011
Estimation in additive models with highly or non-highly correlated covariates • 2010
High dimensional classification • 2010
Variable selection in regression and classification using regularization with concave penalties • 2009
A unified approach to model selection and sparse recovery using regularized least squares • 2009
A unified approach to model selection and sparse recovery using regularized least squares • 2009
High-dimensional classification using features annealed independence rules • 2008
High dimensional covariance matrix estimation using a factor model • 2008
Dynamic integration of time- and state-domain methods for volatility estimation • 2007
Aggregation of nonparametric estimators for volatility matrix • 2007
Volatility matrix estimation and high dimensional classification (Ph.D. dissertation) • 2007
Comment on "Quantile autoregression" • 2006