Statistical Methodology Papers and PresentationsPage 9

Revenue Score: Forecasting Credit Card Products with Zero Inflated Beta Regression and Gradient Boosting

Marc Witarsa, Serasa Experian; Paulo Di Cellio Dias, Serasa Experian

Source category: Conference presentation

Same statistical method different results? Don’t panic the reason might be obvious.

Iryna Kotenko

Source category: Quick Tips

Author collections: Iryna Kotenko

The Rise of Chef Curry: Studying Advanced Basketball Metrics with Quantile Regression in SAS®

Taylor Larkin, The University of Alabama; Denise McManus, The University of Alabama

Source category: Conference presentation

To Hydrate or Chlorinate: A Regression Analysis of the Levels of Chlorine in the Public Water Supply

Drew Doyle, University of Central Florida

Source category: Conference presentation

Using Categorical Variables in Regression Analysis

Jonas Bilenas

Source category: Statistics Data Analysis

File inside source ZIP: Statistics_Data_Analysis/SESUG2017_Paper-29_Final_PDF.pdf

Author collections: Jonas Bilenas

A SAS® Macro for Geographically Weighted Negative Binomial Regression

Alan Silva, University of Brasilia; Thais Rodrigues, University of Brasilia

Source category: Conference presentation

An Ad Hoc Method for Computing Pseudo Effect Size for Mixed Models

Tippey, K. & Longnecker, M.

Source category: Regular Session

Applying Frequentist and Bayesian Logistic Regression to MOOC data in SAS®: a Case Study

Yan Zhang, Educational Testing Service; Ryan Baker, Columbia University; Yoav Bergner, Educational Testing Service

Source category: Conference presentation

Bayesian Inference for Gaussian Semiparametric Multilevel Models

Jason Bentley, The University of Sydney; Cathy Lee, University of Technology Sydney

Source category: Conference presentation

Can You Stop the Guesswork in Your Marketing Budget Allocation? Marketing Mixed Modeling Using SAS® Can Help!

Delali Agbenyegah, Alliance Data Systems

Source category: Conference presentation

Competing-Risks Analyses: An Overview of Regression Models

Joseph Gardiner, Michigan State University

Source category: Conference presentation

Correcting the Quasi-complete Separation Issue in Logistic Regression Models

Xinghe Lu, Amerihealth Caritas

Source category: Conference presentation

Cox proportional hazards regression to model the risk of outcomes per double increase in a continuous explanatory variable

Seungyoung Hwang

Source category: Statistics and Pharmacokinetics

Author collections: Seungyoung Hwang

Finding and Evaluating Multiple Candidate Models for Logistic Regression

Bruce Lund, Magnify Analytic Solutions, Division of Marketing Associates

Source category: Conference presentation

Fitting Multilevel Hierarchical Mixed Models Using PROC NLMIXED

Raghavendra Kurada, SAS

Source category: Conference presentation

Fitting Your Favorite Mixed Models with PROC MCMC

Gordon Brown, SAS; Fang K. Chen, SAS; Maura Stokes, SAS

Source category: Conference presentation

Make Predictions More Predictive: Lift Retail Marketing Campaign Response by Blending Logistic Regression and the Cox Proportional Hazards Model

Hsin-Yi Wang, Alliance Data Systems

Source category: Conference presentation

Mixture Priors 101: Using SAS® to Obtain Powerful Frequentist Inferences with Bayesian Methods

Tyler Hicks and Jeffrey Kromrey

Source category: Statistics/Data Analysis

File inside source ZIP: Statistics and Data Analysis/SD-152_Final_PDF.pdf

Author collections: Tyler Hicks and Jeffrey Kromrey

Predicting Human Activity Sensor Data Using an Auto Neural Model with Stepwise Logistic Regression Inputs

Venkata Vennelakanti, Oklahoma State University

Source category: Conference presentation

Quantitle Regression versus Ordinary Least Squares Regression

Ruth Croxford, Institute for Clinical Evaluative Sciences

Source category: Conference presentation