| PHUSE APAC |
2026 |
Elastic Net Meets BART: A Hybrid Virtual Twins Approach for Reliable Subgroup Identification |
Alexis Bernard Dalam |
source presentation source |
10 |
| PHUSE US |
2025 |
Streamlining Machine Learning Modelling in Neuroscience Clinical Trials Through Cross-Functional Collaboration |
Jing Dai |
source presentation source |
5 |
| PharmaSUG |
2021 |
Mitigating Multicollinearity: Using Regulation Techniques to Minimize the Effect of Collinearity |
Deanna Naomi Schreiber-Gregory |
source |
23 |
| SGF |
2021 |
Next-Generation Data Mining and Machine Learning: Python and SAS Together at Last |
George S. Habek, Suman Kumar Das |
source presentation |
24 |
| SGF |
2020 |
A Survey of Methods in Variable Selection and Penalized Regression |
Yingwei Wang |
source |
17 |
| PharmaSUG |
2019 |
Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets |
Deanna Naomi Schreiber-Gregory, Karlen Bader |
source |
23 |
| SGF |
2019 |
Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets |
Deanna Naomi Schreiber-Gregory |
source |
23 |
| PHUSE US |
2019 |
Integrative Analysis of Circulating Tumor Cell Counts and Gene Expression Levels |
Yauheniya Cherkas |
source presentation source |
8 |
| SESUG |
2018 |
Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets |
Deanna Naomi Schreiber-Gregory, Henry M. Jackson |
source |
23 |
| MWSUG |
2018 |
Regularization Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets |
Deanna Naomi Schreiber-Gregory, Karlen Bader |
|
23 |
| WUSS |
2018 |
Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets |
Deanna Naomi Schreiber-Gregory |
source |
23 |
| SGF |
2018 |
Penalized Variable Selection and Quantile Regression in SAS : An Overview |
Russell M. Lavery |
source |
28 |
| SGF |
2017 |
N-Stage Machine Learning Analysis with the LUA Procedure Helps Solve Big Data Analysis Problems |
Ryo Kiguchi, Eri Sakai, Yoshitake Kitanishi, Akio Tsuji |
source |
8 |
| MWSUG |
2016 |
An Animated Guide: Penalized variable selection techniques in SAS and Quantile Regression |
Russell M. Lavery, Peter Flom |
|
24 |
| SGF |
2016 |
Ensemble Modeling: Recent Advances and Applications |
Wendy Czika, Ye Liu |
source |
20 |
| WUSS |
2015 |
Introduction to SAS Quantile Regression (PROC QuantSelect and PROC QuantReg ) |
Peter L. Flom |
source |
25 |
| SGF |
2015 |
Lasso Regularization for Generalized Linear Models in Base SAS Using Cyclical Coordinate Descent |
Robert Feyerharm |
source |
10 |
| SESUG |
2014 |
Penalizing Your Models: An Overview of the Generalized Regression Platform |
Michael Crotty, Clay Barker |
source |
8 |
| MWSUG |
2011 |
JMP as an Analytic Hub: Using JMP to Build Custom Applications via SASand R |
Kelci J. Miclaus, Clay Barker, Jun Ge |
|
13 |