Research supported in part by NIH R01-HL152439 and NSF DMS-2113589.

Kernelized discriminant analysis for multivariate categorical response regression.
Jin, Y., Zhang, X. and Molstad, A. J. (2023+)
Submitted.

Heterogeneity-aware integrative analyses for ancestry-specific association studies.
Molstad, A. J., Cai, Y., Reiner, A., Kooperberg, C., Sun, W., and Hsu, L. (2023+)
Submitted.
[pdf]

High-dimensional multi-response linear discriminant analysis.
Deng, K., Zhang, X., and Molstad, A. J. (2023+)
Submitted.

Direct covariance matrix estimation with compositional data.
Molstad, A. J., Ekvall, K. O., and Suder, P. M. (2022+)
Submitted.
[pdf][software]

Conditional probability tensor decompositions for multivariate categorical response regression.
Molstad, A. J. and Zhang, X. (2022+)
Submitted.
[pdf][software]

Multiresolution categorical regression for interpretable cell type annotation.
Molstad, A. J. and Motwani, K. (2023+)
Biometrics, to appear.
[pdf][software][example]

Binned multinomial logistic regression for integrative cell type annotation.
Motwani, K., Bacher, R., and Molstad, A. J. (2023+)
Annals of Applied Statistics, to appear.
[pdf][software][example][data]

A convex-nonconvex strategy for grouped variable selection.
Liu, X., Molstad, A. J., and Chi, E. C. (2023+)
Electronic Journal of Statistics, to appear.
[pdf][software][example]

Dimension reduction for integrative survival analysis.
Molstad, A. J. and Patra, R. K. (2023)
Biometrics, 79 (3), 1610-1623.
[pdf][reproduce simulations]

A likelihood-based approach for multivariate categorical response regression in high dimensions.
Molstad, A. J. and Rothman A. J. (2023)
Journal of the American Statistical Association, 118 (541), 1402-1414.
[pdf][software][example]

Mixed-type multivariate response regression with covariance estimation.
Ekvall, K. O. and Molstad, A. J. (2022)
Statistics in Medicine, 41 (15), 2768-2785.
[pdf][software]

New insights for the multivariate square-root lasso.
Molstad, A. J. (2022)
Journal of Machine Learning Research, (66), 1−52.
[pdf][software][example]

Scalable algorithms for semiparametric accelerated failure time models in high dimensions.
Suder, P. M. and Molstad, A. J. (2022)
Statistics in Medicine, 41 (6), 933-949.
[pdf][software][example]

A covariance-enhanced approach to multi-tissue joint eQTL mapping with application to transcriptome-wide association studies.
Molstad, A. J., Sun, W., and Hsu, L. (2021)
Annals of Applied Statistics, 15 (2), 998-1016.
[pdf][results]

Estimating multiple precision matrices with cluster fusion regularization.
Price, B. S., Molstad, A. J., and Sherwood, B. (2021)
Journal of Computational and Graphical Statistics, 30 (4), 823-834.
[pdf][supplementary material]

An explicit mean-covariance parameterization for multivariate response linear regression.
Molstad, A. J., Weng, G., Doss, C. R., and Rothman, A. J. (2021)
Journal of Computational and Graphical Statistics, 30 (2), 612-621.
[pdf][software][example]

Gaussian process regression for survival time prediction with genome-wide gene expression.
Molstad, A. J., Hsu, L., and Sun, W. (2021)
Biostatistics, 22 (1), 164-180.
[pdf][software][example]

Asymptotic properties of concave L1-norm group penalties.
Sherwood, B., Molstad, A. J., and Singha, S. (2020)
Statistics and Probability Letters, 157
[pdf][supplementary material]

A penalized likelihood method for classification with matrix-valued predictors.
Molstad, A. J. and Rothman, A. J. (2019)
Journal of Computational and Graphical Statistics, 28 (1), 11-22.
[pdf][software][supplementary material]

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

Indirect multivariate response linear regression.
Molstad, A. J. and Rothman, A. J. (2016)
Biometrika, 103 (3), 595-607.
[pdf][supplementary material]