Lee Dicker

Home        Research        Teaching        Links        CV             


Journal and conference publications

Research interests


  1. High-dimensional data analysis

  2. -Variance estimation and random-effects models

  3. -Applications of random matrix theory

  4. Nonparametric empirical Bayes and mixture models

  5. Statistical analysis of genomic and proteomic data

  6. Statistics in finance

  7. Diabetes technology

  1. 1.Dicker, L. H. and Erdogdu, M. A. (2016+) “Flexible results for quadratic forms with applications to variance components estimation.Annals of Statistics. Accepted.

  2. 2.Dicker, L. H. and Erdogdu, M. A. (2016+) “Maximum likelihood for variance estimation in high-dimensional linear models.Artificial Intelligence and Statistics (AISTATS 2016). Accepted.

  3. 3.Dicker, L. H. and Zhao, S. D. (2016+) “High-dimensional classification via nonparametric empirical Bayes and maximum likelihood inference.Biometrika. Advance online publication doi: 10.1093/biomet/asv067.

  4. 4.Dicker, L. H. (2016) “Ridge regression and asymptotic minimax estimation over spheres of growing dimension.Bernoulli, 22, 1-37.

  5. 5.Dicker, L. H. (2014) “Variance estimation in high-dimensional linear models.Biometrika, 101, 269-284.

  6. 6.Dicker, L. H. (2014) “Sparsity and the truncated l2-norm.Artificial Intelligence and Statistics (AISTATS 2014), 17, 159-166.

  7. 7.Sofer, T., Dicker, L. H., and Lin, X. (2014) “Variable selection for high-dimensional multivariate outcomes.Statistica Sinica, 24, 1633-1654. 

  8. 8.Li, Y., Dicker, L. H., and Zhao, S. D. (2014) “The Dantzig selector for censored linear regression models.Statistica Sinica, 24, 251-268.

  9. 9.Dicker, L. H. and Foster, D. P. (2013) “One-shot learning and big data with n=2.Neural Information Processing Systems (NIPS 2013), 26, 270-278.

  10. 10.Dicker, L. H., Sun. T, Zhang, C.-H., Keenan, D. B., and Shepp, L. A. (2013) “Continuous blood glucose monitoring: A Bayes-hidden Markov approach.Statistica Sinica, 23, 1595-1627.

  11. 11.Dicker, L. H. (2013) “Optimal equivariant prediction for high-dimensional linear models with arbitrary predictor covariance.Electronic Journal of Statistics, 7, 1806-1834.

  12. 12.Dicker, L. H. and Lin, X. (2013) “Parallelism, uniqueness, and large-sample asymptotics for the Dantzig selector.Canadian Journal of Statistics, 41, 23-35.

  13. 13.Dicker, L. H., Huang, B., and Lin, X. (2013) “Variable selection and estimation with the seamless-L0 penalty.Statistica Sinica, 23, 929-962.

  14. 14.Fu, S., Yang, L., Li, P., Hofmann, O., Dicker, L. H., Hide, W., Lin, X., Watkins, S. M., Ivanov, A. R., and Hotamisligil, G. S. (2011) “Aberrant lipid metabolism disrupts calcium homeostasis causing liver endoplasmic reticulum stress in obesity.Nature, 473, 528-531.

  15. 15.Clancy, R. R., Dicker, L. H., Cho, C., Nicolson, S. C., Wernovsky, G., Spray, T. L., and Gaynor, J. W. (2011) “Agreement between long-term neonatal background classification by conventional and amplitude-integrated EEG.Journal of Clinical Neurophysiology, 28, 1-9.

  16. 16.Dicker, L. H., Lin, X., and Ivanov, A. R. (2010) “Increased power for the analysis of label-free LC-MS/MS proteomic data by combining spectral counts and peptide peak attributes.Molecular and Cellular Proteomics, 9, 2704-2718.

  17. 17.Wallach, H. M., Jensen, S. T., Dicker, L. H., and Heller, K. (2010) “An alternative prior process for nonparametric Bayesian clustering.Artificial Intelligence and Statistics (AISTATS 2010), 13, 892-899.

  18. 18.Lin, X. and Dicker, L. H. (2009) “Discussion of ‘Parametric versus nonparametrics: Two alternative methodologies.’Journal of Nonparametric Statistics, 21, 415-417.

Selected preprints

Recent awards

  1. National Science Foundation CAREER Award, 2015.
    Project title: Maximum likelihood and nonparametric empirical Bayes methods in high dimensions.