Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results. Using AI to Improve Productivity and Quality Prof. Haim Bar Friday, March 27, 12:00 pm AUST 326 […]

Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results. Using AI to Improve Productivity and Quality Prof. Haim Bar Friday, March 27, 12:00 pm AUST 326 […]

Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results. MPCR: Multi- and Mixed-Precision Computations Package in R Prof. Mary Lai Salvana Paper: https://arxiv.org/abs/2406.02701 Friday, May 2, 12:00 […]

Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results. Code got slow?  Maybe it’s time to try Julia. Prof. HaiYing Wang Friday, March 28, 12:00 pm […]

Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results. How to annoy a journal editor – top 10 typesetting mistakes when submitting a paper in LaTeX […]

Statistical Hypothesis from the view point of Fisher, Jeffreys and Neyman: Agreements and Disagreements

Meng, X. L. (1994). Multiple-Imputation Inferences with Uncongenial Sources of Input . Statistical Science, 538-558.

David M. Blei, Alp Kucukelbir & Jon D. McAuliffe (2017) Variational Inference: A Review for Statisticians, Journal of the American Statistical Association, 112:518, 859-877.

Ghosh, M., Sinha, B. K. and Mukhopadhyay, N. (1976). Multivariate sequential point estimation. Journal of Multivariate Analysis 6: 281-294.

Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences, 116(32), 15849-15854.