Adam J. Rothman

Professor & Director of Graduate Studies
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 (2015-2021) 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.

Selected awards
National Science Foundation CAREER award, 2015.
Student Paper Competition Award, Computing and Graphics Sections ASA, 2008.

Editorial boards
Electronic Journal of Statistics 2022--.
Journal of Computational and Graphical Statistics 2021--.
Journal of the American Statistical Association 2014-2017.

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)

Selected papers
Zhao, H., Molstad, A. J., and Rothman, A. J. (2024). Subspace decompositions for association structure learning in multivariate categorical response regression. Submitted.

Ham, D. Y. and Rothman, A. J. (2024). Fitted value shrinkage. Electronic Journal of Statistics 18, 4499-4525.

Molstad, A. J. and Rothman, A. J. (2023). A likelihood-based approach for multivariate categorical response regression in high dimensions. Journal of the American Statistical Association 118(542), 1402-1414.

Molstad, A. J., Weng, G., Doss, C. R., and Rothman, A. J. (2021). An explicit mean-covariance parameterization for multivariate response linear regression. Journal of Computational and Graphical Statistics 30(3), 612-621.

Price, B. S., Geyer, C. J., and Rothman, A. J. (2019). Automatic response category combination in multinomial logistic regression. Journal of Computational and Graphical Statistics 28(3), 758-766.

Molstad, A. J. and Rothman, A. J. (2018). Shrinking characteristics of precision matrix estimators. Biometrika 105(3), 563-574.

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

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.

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). 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

2024/11.