JSM 2019

The annual Joint Statistical Meetings (JSM) is a key scientific conference for SAS, and this year in Denver is no exception. JSM is one of the largest statistical events in the world, bringing together thousands of attendees from more than 50 countries in academia, industry and government.

More than 50 SAS employees will be attending JSM, from R&D, Technical Support, Publications and Education, as well as a strong contingent from JMP. Below is a list of the many ways that SAS employees will be contributing directly to the scientific content of the JSM. To see more detail, visit the JSM 2019 online program.

Short Courses

“A First Step into Deep Learning for Computer Vision” Robert Blanchard and Brett Wujek
Causal Effect Estimation with Observational Data: Planning and Practice" Michael Lamm and Clay Thompson
“Essential Bayes: Paradigm, Techniques, and Applications" Fang Chen and Amy Shi
Preparing Statisticians and Data Scientists for Leadership: How to See the Big Picture and Have More Influence" Fang Chen

Computer Technology Workshops

“Introducing the SAS BGLIMM Procedure for Bayesian Generalized Linear Mixed Models" Amy Shi
“Practical Causal Mediation Analysis with PROC CAUSALMED in SAS/STAT" Yiu-Fai Yung
“Analysis of Restricted Mean Survival Time Using SAS/STAT" Changbin Guo
“Geospatial Analysis Using SAS Software" Pradeep Mohan
“Visual Interaction, Statistical Analysis and Machine Learning to Advance Life Science Research with JMP Software" Kelci Miclaus and Ruth Hummel
Flexible, Interactive (Generalized Regression) Modeling with JMP Pro" Ruth Hummel and Clay Barker

Invited Talks

“Interpolating Distributions for Populations in Nested Geographies using Public-use Data with Application to the American Community Survey" Matthew Simpson
“Analysis and Evaluation of Covering Arrays Using Prior Information" Ryan Lekivetz and Joseph Morgan
“Visualizing Covering Arrays Using Design Fractals" Caleb King, Joseph Morgan, and Ryan Lekivetz

Papers, Posters, and Roundtables

“Estimate of Treatment Difference for Non-Normally Distributed Data in Clinical Trials – Comparison of Hodges-Lehmann Method and Quantile Regression" Yonggang Yao
“Advanced Visualization Techniques for Big Data" Scott Wise
“Feature Level Sentiment Analysis using SAS" Da Young Lee, JeeHyun Hwang and Xu Yang
“Language Modeling using SAS" JeeHyun Hwang, Xu Yang, and Haipeng Liu
"Modern Bayesian Software: Current Capability and Usage Fang Chen
“For the Love of Crocs: Text Mining Product Reviews" Ruth Hummel and Mia L. Stephens
“Measurement Systems Analysis for Functional Data" Laura Lancaster and Chris Gotwalt
“Extracting Practical Value from Experimental Designs through Simulation" Rob Lievense
“Applying Dynamic Interactive Visualization for Statistical Discovery in JMP" Boyd Alexander Gregg