In Part 1 of this series, we asked three of our bestselling authors, Ron Cody, Kevin Smith and Xiangxiang Meng, for their favorite papers from SAS Global Forum 2018.

Here are four more choices from some familiar faces:

Lora Delwiche, co-author of The Little SAS® Book series

I liked the paper, Using SAS® OnDemand for Academics: Ten Tips for Success, by Randy Mullis from SAS. SAS® OnDemand for Academics is a great resource for people who want to learn SAS but don't have access to SAS software at home or work. I think a lot of people don't know about SAS OnDemand and those who do, think you need to be a university student to use it. In fact, anyone who wants to learn SAS can create an account and all you need is an Internet connection. SAS OnDemand is also great for teaching SAS, and Randy's paper gives several tips for instructors to help make things go more smoothly.

 

Susan Slaughter, co-author of The Little SAS® Book series

My favorite paper from SGF 2018 is AnnMaria De Mars' paper, Bricolage: My Autobiography, with SAS® Procedures. In this entertaining and thought-provoking paper, AnnMaria describes her nonlinear and unconventional career path. What fascinates me is that while AnnMaria's particular path is unique, having unconventional career paths is common for SAS programmers. There is something about SAS that helps to break down barriers.

 

David Dickey, co-author of SAS® for Forecasting Time Series, Third Edition

From 2012, Tips and Strategies for Mixed Modeling with SAS/STAT® Procedures, by Kathleen Kiernan, Jill Tao, and Phil Gibbs, SAS. Mixed models have become quite popular in statistics. The models are complex and their estimation must be done carefully. I like this paper because it deals with some of the problems that can arise when using a complex and sophisticated procedure and gives practical advice on how to solve them. (I usually mention this paper when teaching the mixed models course.)

Another favorite paper is, Insights into Using the GLIMMIX Procedure to Model Categorical Outcomes with Random Effects, by Kathleen Kiernan, SAS. This is a complete how-to guide for performing analyses and troubleshooting with sophisticated generalized linear mixed models (that is, models for non-normal response variables that include random predictor variables).

All great selections! If you enjoyed exploring these papers, then check out our free e-book special collections - carefully curated collections of papers around key topics such as machine learning, fraud analytics, SAS® Viya®, the Internet of Things, and more.