Adam J. Rothman

Associate Professor
School of Statistics
University of Minnesota
224 CHURCH ST SE RM 313
MINNEAPOLIS MN 55455-0460



Academic background
B.S.E. (Electrical Engineering) Cum Laude, University of Michigan, 2005.
Ph. D. (Statistics) advised by Elizaveta Levina and Ji Zhu, University of Michigan, 2010.

Research supported in part by the National Science Foundation DMS-1452068 (2015--) and DMS-1105650 (2011-2015); and the Yahoo! PhD student fellowship (2008-2010).

Software
PDSCE - an R package to compute and select tuning parameters for two positive definite and sparse covariance estimators.

MRCE - an R package to compute and select tuning parameters for the sparse multiple output regression coefficient matrix estimator that accounts for the error covariance matrix (the MRCE estimator).

abundant - an R package that fits high-dimensional principal fitted components models.

Past PhD students
Aaron J. Molstad, PhD 2017
Bradley S. Price, PhD 2014 (coadvised by Charles J. Geyer)
Christopher D. Desjardins, PhD 2013 (his primary advisor was Michael R. Harwell in Educational Psychology)

Papers
Molstad, A. J. and Rothman, A. J. (2017). Shrinking characteristics of precision matrix estimators. Submitted.

Price, B. S., Geyer, C. J., and Rothman, A. J. (2017). Automatic response category combination in multinomial logistic regression. Submitted.

Molstad, A. J. and Rothman, A. J. (2016). A penalized likelihood method for classification with matrix-valued predictors. Submitted.

Molstad, A. J. and Rothman, A. J. (2016). Indirect multivariate response linear regression. Biometrika 103(3), 595-607. supplement

Price, B. S., Geyer, C. J., and Rothman, A. J. (2015). Ridge fusion in statistical learning. Journal of Computational and Graphical Statistics 24(2), 439-454. R package

Rothman, A. J. and Forzani, L. (2014). On the existence of the weighted bridge penalized Gaussian likelihood precision matrix estimator. Electronic Journal of Statistics 8, 2693-2700.

Cook, R. D., Forzani, L., and Rothman, A. J. (2013). Prediction in abundant high-dimensional linear regression. Electronic Journal of Statistics 7, 3059-3088.

Bickel, P. J., Levina, E., Rothman, A. J., and Zhu, J. (2012). Discussion of "Minimax estimation of large covariance matrices under L1-Norm" by T. Cai and H. Zhou. Statistica Sinica 22(4), 1367-1370.

Rothman, A. J. (2012). Positive definite estimators of large covariance matrices. Biometrika 99(3), 733-740. R package

Cook, R. D., Forzani, L., and Rothman, A. J. (2012). Estimating sufficient reductions of the predictors in abundant high-dimensional regressions. Annals of Statistics 40(1), 353-384. supplement, errata R package

Rothman, A. J., Levina, E., and Zhu, J. (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics 19(4), 947-962. R package

Rothman, A. J., Levina, E., and Zhu, J. (2010). Discussion of "Stability selection" by N. Meinshausen and P. Buhlmann. Journal of the Royal Statistical Society B 72(4), 465-467.

Rothman, A. J., Levina, E., and Zhu, J. (2010). A new approach to Cholesky-based covariance regularization in high dimensions. Biometrika 97(3), 539-550. R package

Rothman, A. J., Levina, E., and Zhu, J. (2009). Generalized thresholding of large covariance matrices. Journal of the American Statistical Association 104(485), 177-186.

Rothman, A. J., Bickel, P. J., Levina, E., and Zhu, J. (2008). Sparse permutation invariant covariance estimation. Electronic Journal of Statistics 2, 494-515. note

Levina, E., Rothman, A. J., and Zhu, J. (2008). Sparse estimation of large covariance matrices via a nested lasso penalty. Annals of Applied Statistics 2(1), 245-263.



Modified 2017-05-10