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119 papers; 108 titles.

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Conference × Year × Title × Authors Extras Pages
WUSS 2026 Demystifying Regression Analysis Techniques with R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 35
PharmaSUG 2026 Regression Analysis Made Easy Using R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 source 32
WUSS 2024 Analyzing Goalkeeper Impact in Premier League Football Alec Troy Christiansen,
Benjamin T Houser		 source 10
WUSS 2023 What's next after model comparison? Model selection and model averaging in SAS Chong Ho Yu,
Charlene Yang		 source 14
PHUSE EU 2019 Symbolic Regression: Survival of the Best Fit Hector Leitch source
presentation source		 9
MWSUG 2019 Comparison of three methods for transforming predictor variables to improve model fit using SAS Douglas Thompson 13
MWSUG 2019 From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing Troy Martin Hughes 22
SGF 2019 A Comparison of Algorithmic and Epidemiologic Approaches for Modeling Predictors of Type II Diabetes Audrey Whittle source 8
SGF 2019 Airbnb New York City: Demystifying the Superhost Program Soumya Ranjan Kar Choudhury source 7
SGF 2019 Asking the Right Questions: A study in Multiclass Imbalanced Classification for H1-B Visas Matthew Bunker,
Owen Kelly		 source 7
SGF 2019 Predicting Patients at High Risk of Hospital-Acquired Conditions Using the LOGISTIC Procedure Jennifer Hargrove,
Pamela Hipp,
Glenn Sidle		 source 12
SCSUG 2018 Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model Mei Najim source 17
WUSS 2018 Combined Predictor Selection for Multiple Clinical Outcomes Using PHREG L. Grisell Diaz-Ramirez,
Siqi Gan,
Sei J. Lee,
Alexander Smith,
John Boscardin		 source 14
WUSS 2018 From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing Troy Martin Hughes source 22
PharmaSUG 2018 From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing Troy Martin Hughes source 23
SGF 2018 Claim Analytics: A Litigation Prediction Case Study Mei Najim source 18
SGF 2018 Working Together for Public Health: Using SAS , Esri, and Tableau to Enhance Data Presentation Jennifer L. Han,
Miriam McGaugh		 source 135
SESUG 2017 A macro of building predictive model in PROC LOGISTIC with AIC-optimal variable selection embedded in cross-validation Hongmei Yang source 7
SESUG 2017 From FREQing Slow to FREQing Fast: Facilitating a Four-Times-Faster FREQ with Divide-and-Conquer Parallel Processing Troy Martin Hughes source 22
PHUSE EU 2017 The Model Statistician Insights into Model Selection Techniques in Clinical Trials Peter Williams source
presentation source		 6
MWSUG 2017 Claim Analytics Mei Najim 18
MWSUG 2017 How Can an NBA Player Be Clutch?: A Logistic Regression Analysis Logan Edmonds 18
WUSS 2017 A Data Mining Approach for Developing Intervention Programs to Lessen Body Dissatisfaction and Promote Healthy Weight Management Pitipa Chongwatpol,
Jongsawas Chongwatpol		 source 12
PharmaSUG 2017 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 20
SGF 2017 PROC LOGISTIC: Using New SAS 9.4 Features for Cumulative Logit Models with Partial Proportional Odds Paul Hilliard source
zip
pdf		 16
SGF 2017 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim source 17
SGF 2017 Using SAS to Analyze Emergency Department Visits: Medicaid Patients Compared to Other Pay Sources Bradley Casselman,
Taylor K. Larkin,
Denise J. McManus		 source 16
SCSUG 2016 Effective method on curing heart disease K. Varadarajan,
Surya Bhaskar Ayyalasomayajula		 source 12
SESUG 2016 Factors of Multiple Fatalities in Car Crashes Bill Bentley,
Gina Colaianni,
Kennedy Onzere,
Cheryl Joneckis,
Xuelei (Sherry) Ni		 source 62
SESUG 2016 Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software Robert N. Rodriguez source 23
SESUG 2016 Using JMP to apply Decision Trees and Random Forests as Screening Tools for Limiting Candidate Predictors in Regression Models Jason Brinkley source 10
MWSUG 2016 A Demonstration of Various Models Used in a Key Driver Analysis Steven M. LaLonde 17
MWSUG 2016 An Analysis of the Repetitiveness of Lyrics in Predicting a Song's Popularity Drew Doyle 12
MWSUG 2016 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim 17
MWSUG 2016 Regression Analysis of the Levels of Chlorine in the Public Water Supply in Orange County, FL Drew Doyle 11
WUSS 2016 Introduction to Survival Analysis in SAS Leanne Goldstein,
Rebecca Ottesen		 source 19
WUSS 2016 Statistical Model Building for Large, Complex Data: Four New Directions in SAS/STAT Software Robert N. Rodriguez source 23
SGF 2016 A Property and Casualty Insurance Predictive Modeling Process in SAS Mei Najim source 14
SGF 2016 Comparative Regional Analysis of Bacterial Pneumonia Readmission Patients in the Medicare Category Heramb Joshi,
Goutam Chakraborty,
William Paiva,
Aditya Sharma		 source 12
SGF 2016 Factors of Multiple Fatalities in Car Crashes Bill Bentley,
Gina Colaianni,
Cheryl Joneckis,
Sherry Ni,
Kennedy Onzere		 source 60
SGF 2016 Predicting the Current Market Value of a Housing Unit across the Four Census Regions of the United States Using SAS Enterprise Miner 12.3 Mostakim Tanjil,
Goutam Chakraborty		 source
presentation		 12
SGF 2016 Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software Bob Rodriguez source 23
SGF 2016 Survival Analysis of the Patients Diagnosed with Non-Small Cell Lung Cancer Using SAS Enterprise Miner 13.1 Raja Rajeswari Veggalam,
Akansha Gupta,
Goutam Chakraborty		 source 11
MWSUG 2015 A Property & Casualty Insurance Predictive Modeling Process in SAS Mei Najim 14
SESUG 2015 Regression Analysis of the Levels of Chlorine in the Public Water Supply in Orange County, FL Drew Doyle source 10
WUSS 2015 Application of Data Mining in Energy Industry: A Case Study of NOx Prediction Jongsawas Chongwatpol source 9
PharmaSUG CN 2015 A Macro to Automatically Select Covariates from Prognostic Factors and Exploratory Factors for Multivariate Cox PH Model Yu Cheng 6
SGF 2015 Applied Analytics in Festival Tourism: A Case Study of Intention-to-Revisit Prediction in an Annual Local Food Festival in Thailand Jongsawas Chongwatpol,
Thanathorn Vajirakachorn		 source 13
SESUG 2014 VIF Regression: A SAS Application to Feature Selection in Large Data Sets Ruiwen Zhang 5
PharmaSUG 2014 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 19
SGF 2014 A Stepwise Algorithm for Generalized Linear Mixed Models Nagaraj Neerchal,
Jorge Morel,
Xuang Huang,
Alain Moluh		 source 11
SGF 2014 Dining with the Data: The Case of New York City and Its Restaurants Pruthvi Bhupathiraju Venkata,
Goutam Chakraborty		 source 7
SGF 2014 Improving the Thermal Efficiency of Coal-Fired Power Plants: A Data Mining Approach Thanrawee Phurithititanapong,
Jongsawas Chongwatpol		 source 12
SGF 2014 Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers Sai Vijay Kishore Movva,
Vandana Reddy,
Goutam Chakraborty		 source 11
SGF 2014 Reevaluating Policy and Claims Analytics: A Case of Corporate Fleet and Non-Fleet Customers in the Automobile Insurance Industry Kittipong Trongsawad,
Jongsawas Chongwatpol		 source 10
SGF 2014 SAS Data Mining for Predictor Identification: Developing Strategies for High School Dropout Prevention Wendy B. Dickinson,
Morgan C. Wang		 source 9
WUSS 2013 Optimism of Best Subset Selection by AIC/BIC for Prognostic Model Building Yinghui Miao,
Irena Stijacic Cenzer,
Katharine Kirby,
W. John Boscardin		 source 13
WUSS 2013 Survival 101 - Just Learning to Survive Leanne Goldstein,
Rebecca Ottesen		 source 24
SGF 2013 Bringing Churn Modeling Straight to the Source: SAS and Teradata In-Database Model Development Jonathan Wexler,
Karl Krycha		 source 9
SGF 2013 Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples Irena Stijacic Cenzer,
W. John Boscardin,
Katharine Kirby,
Yinghui Miao		 source 12
PHUSE EU 2012 The Logic and Logistics of Logistic Regression, Including New Features in SAS 9.2 Lawrence Rasouliyan source
presentation source		 16
MWSUG 2012 An Analysis of Diabetes Risk Factors Using Data Mining Approach Akkarapol Sa-ngasoongsong,
Jongsawas Chongwatpol		 11
WUSS 2012 The Steps to Follow in a Multiple Regression Analysis Theresa H. Ngo source 12
PharmaSUG 2012 Automated forward selection for Generalized Linear Models with Categorical and Numerical Variables using PROC GENMOD Manuel Sandoval source 5
SGF 2012 Its About Time: Discrete Time Survival Analysis using SAS Enterprise Miner Sascha Schubert,
Susan Haller,
Taiyeong Lee		 source 15
SGF 2012 The Steps to Follow in a Multiple Regression Analysis Theresa H. Ngo source 12
MWSUG 2011 SAS Model Selection Macros for Complex Survey Data Using PROC SURVEYLOGISTIC/SURVEYREG Fang Wang,
Hee-Choon Shin		 27
SESUG 2010 Using SAS Text Miner 4.1 to create a term list for patients with PTSD within the VA Matthew R. Richardson source 9
SESUG 2009 Developing Business Failure Prediction Models Using SAS Software Oki Kim source 10
PNWSUG 2009 Building Multiple Linear Regression Models - Food for Thought Colleen E. McGahan,
Mei Chong		 11
NESUG 2009 Stopping Stepwise: Why Stepwise Selection Methods are Bad and What you Should use Instead David L. Cassell,
Peter L. Flom		 7
WUSS 2009 Developing Business Failure Prediction Models Using SAS Software Oki Kim source 10
WUSS 2009 Discriminant Analysis, a Powerful Classification Technique in Predictive Modeling George C. J. Fernandez source 20
SGF 2009 Applications of the GLMSELECT Procedure for Megamodel Selection Robert A. Cohen source 25
SGF 2009 Source of Nitrogen in Q-BOP Steel (Statistical Analysis) Arkady M. Kushnir source 19
WUSS 2008 Evaluation of a Hospice Care Referral Program Using Cox Proportional Hazards Model Lida Gharibvand,
Daniel R. Jeske,
Solomon Liao		 source 16
MWSUG 2008 Classification of Breast Cancer Cells Using JMP Marie Gaudard 34
PNWSUG 2008 Stopping Stepwise: Why Stepwise and Similar Selection Methods Are Bad, and What You Should Use David L. Cassell 7
PharmaSUG 2008 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 14
PharmaSUG 2008 TPHREG and GLMSELECT: Using the CLASS statement in SAS 9.1 Laura E. Archer 11
SGF 2008 Advanced Statistical and Graphical features of SAS PHREG Lida Gharibvand,
George C. J. Fernandez		 source 12
NESUG 2007 Stopping Stepwise: Why Stepwise and Similar Selection Methods Are Bad, and What You Should Use Peter Flom,
David L. Cassell		 7
SESUG 2007 Information Criteria Methods in SAS for Multiple Linear Regression Models Dennis J. Beal source 10
SESUG 2007 Integrating SAS and Microsoft .NET for Data Analysis Mai Nguyen,
Shane Trahan,
Patricia Nguyen,
Jonathan Cirella		 source 10
MWSUG 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga 20
WUSS 2007 Data Mining Using SAS Enterprise Miner Randall Matignon source 10
SGF 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga source 20
SGF 2007 Predictive Modeling in Higher Education Tom Bohannon source 14
SGF 2007 Variable Screening for Multinomial Logistic Regression on Very Large Data Sets as Applied to Direct Response Modeling John A. Cherrie source 12
SCSUG 2006 What's New in SAS Enterprise Miner 5.2 Wayne Thompson,
David R. Duling		 10
WUSS 2006 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 source 14
SUGI 2006 Application of Proc Discrim and Proc Logistic in Credit Risk Modeling Jin Li source 9
SUGI 2006 Introducing the GLMSELECT Procedure for Model Selection Robert A. Cohen source 18
SUGI 2006 What's New in SAS Enterprise Miner 5.2 Wayne Thompson,
David R. Duling		 source 10
SESUG 2005 SAS Code to Select the Best Multiple Linear Regression Model for Multivariate Data Using Information Criteria Dennis J. Beal,
Oak Ridge		 source 6
SUGI 2005 Model Building in PROC PHREG with Automatic Variable Selection and Information Criteria Emily M. Cain,
Ken Kleinman,
Ernest S. Shtatland		 source 10
SUGI 2004 A New Strategy of Model Building in SAS PROC LOGISTIC with Automatic Variable Selection, Validation, Shrinkage, and Model Averaging Ernest S. Shtatland,
Ken Kleinman,
Emily M. Cain		 source 11
SUGI 2003 SAS Macros and Tools for Working with Weighted Logistic Regression Models That Use Survey Data David Izrael,
Annabella A. Battaglia,
David C. Hoaglin,
Michael P. Battaglia		 source 10
SUGI 2003 STEPWISE Methods in Using SAS PROC LOGISTIC and SAS Enterprise MinerTM for Prediction Ernest S. Shtatland,
Ken Kleinman,
Emily M. Cain		 source 6
PharmaSUG 2002 Macros for Model Building, Summarizing, and Displaying Results of Stepwise Logistic Regressions Lori S. Parsons 6
SUGI 2002 Macros for Model Building, Summarizing, and Displaying Results of Stepwise Logistic Regressions Lori S. Parsons source 6
SCSUG 2001 Heel Ultrasound As a Predictor of Appendicular Bone Mineral Density Rebecca G. Frederick,
E. Barry Moser,
Ellen R. Brooks		 5
SUGI 2001 The Perils of Stepwise Logistic Regression and How to Escape Them Using Information Criteria and the Output Delivery System Ernest S. Shtatland,
Mary B. Barton,
Emily M. Cain		 source 6
SESUG 2000 Using Data Mining Technology to Identify and Prioritize Emerging Opportunities for Mortgage Lending to Homeownership-Deficient Communities Mortgage Lending source 10
NESUG 2000 How to Be a Bayesian in SAS: Model Selection Uncertainty in PROC LOGISTIC and PROC GENMOD Ernest S. Shtatland 9
WUSS 2000 An Overview of remedial tools for collinearity in SAS Chong Ho Yu source 6
PNWSUG 1998 Stepwise Anything: Reading in Regression Procedure Output for Further Testing Ted Clay,
Linda Remy		 10
SUGI 1998 Multivariable Cox Proportional Hazard Model by SAS PHREG &Validation by Bootstrapping Using SAS Randomizers With Applications Involving Electrocardiology and Organ T ransplants Sudhanshu K. Ghoshal source 6
WUSS 1997 Cross-Validation Estimates of the Prediction Error in a Random-Coefficients Mixed Model Used to Estimate Emissions from On-Road Motor Vehicles using SAS Steven Brisby source 5
NESUG 1997 Implementation and Comparison of the Back Propagation Neural Network in SAS Software John S. DaPonte 10
SUGI 1997 Developing Risk Adjustment Techniques Using the SAS System for Assessing Health Care Quality in the IMSystem Yanchun Xu,
Andrius Kubilius		 source 5
SESUG 1996 Stepwise Regression as a Multiple Comparison Procedure Rodney Sparapani 3
WUSS 1995 Applying the SAS System in Undergraduate Managerial Economics: A General Approach Seid M. Zekavat source 5
SUGI 1995 Bootstrap Cox Regression Using SAS Procedures Chikuma Hamada,
Junji Kishimoto		 source 5
SEUGI 1993 Variable Selection Techniques Implemented in Procedures of SAS Software Olaf Gefeller source 9
SUGI 1987 Using SAS Software to Develop a Prototype for Determining Participant Profiles and for Assessing Potential Relationships Among Health Insurance Variables Ralph F. Catalanello,
Bruce W. Eagle		 source 6
SUGI 1985 Comparison of Multivariate Regression Analysis between Logistic Model and the Least Square Model Using SAS Software Jane R. Bratz,
J.S. Kim,
George K. Tokuhata		 source 4
SUGI 1984 Developing A Computer Cost Distribution Method Using the MICS Data Base Anne N. Bluth,
Jean L. Waits		 source 6
SUGI.ONE 1976 Reply to Comments on a Comparison of BMD, SAS and SPSS & The Relationship of SAS to Other Packages James W. Frane,
Jonathan B. Fry,
James H. Goodnight,
T. Papaioannou,
Stewart Robinovitz,
G.P.H. Styan,
Neal Van Eck,
L.L. Ward		 source 10

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