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344 papers and 22 presentations; 349 titles.

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Conference × Year × Title × Authors Extras Pages
WUSS 2026 A Practical Roadmap for the 2026 Enterprise Generative AI Stack: AI Agent Architectures, Frameworks, and Secure Deployment for Regulated Industries Ryan Paul Lafler source 7
WUSS 2026 A Structured Agent-Based Framework for Early-Stage Data Science Pipelines Dheerusha Tiwari,
Siddharth Birajdar		 source 14
WUSS 2026 Demystifying Regression Analysis Techniques with R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 35
WUSS 2026 Interactive Modeling with SAS Visual Statistics Danny R. Modlin source 28
PharmaSUG CN 2026 An Interpretable Machine Learning Framework for Exploratory Survival and Safety Analysis Using Random Survival Forests and SHAP Kun Shao source 17
PharmaSUG 2026 A Practical Roadmap for the 2026 Enterprise Generative AI Stack: AI Agent Architectures, Frameworks, and Secure Deployment Ryan Paul Lafler source 8
PharmaSUG 2026 Regression Analysis Made Easy Using R Zheyuan Yu,
Kirk Paul Lafler,
Zichun Gao,
Jiaxin Xu,
Zeqi Li,
Ruochen Shao		 source 32
PHUSE APAC 2026 Elastic Net Meets BART: A Hybrid Virtual Twins Approach for Reliable Subgroup Identification Alexis Bernard Dalam source
presentation source		 10
PHUSE APAC 2026 Enhancing Real-Time Clinical Reporting with R Shiny and Large Language Models: Automated Code Generation and Dynamic TLG Visualization Mahesh Divakaran source
presentation source		 10
PHUSE APAC 2026 From Data to Digital Phenotypes: Modelling High-Dimensional PROs and Actigraphy Data in Decentralized Psychiatric Trials Sakshi Kaushik source
presentation source		 14
PHUSE APAC 2026 Machine Learning Algorithms to Explore the Relationship Between Primary Obsessive-Compulsive Disorder (OCD) and Co-morbid Depression Symptoms Ajay Kumar Singh,
Sabyasachi Mukherjee		 source
presentation source		 12
PHUSE EU 2025 Talk to Your Data: Accelerating Insights with AI-Powered Dashboards Emily Yates source
presentation source		 8
MWSUG 2025 Feature Selection and Classification Using Fuzzy Logic Ross Bettinger source 39
MWSUG 2025 Large-Scale Time Series Forecasting in Model Studio Danny R. Modlin source 39
SESUG 2025 Large-Scale Time Series Forecasting in Model Studio Danny R. Modlin 39
SESUG 2025 Modernizing Predictive Modeling in SAS: When to Move Beyond PROC GLM Duha Khan,
Sriya Venkat,
Sydney McDermott		 8
WUSS 2025 Large-Scale Time Series Forecasting in Model Studio Danny R. Modlin source 39
PharmaSUG 2025 Implementation of the inverse probability of censoring weighting (IPCW) Model in Oncology Trials Mei Huang,
Shibani Harite,
Haijun Ma,
Linsong (Athena) Zhang		 source 17
PharmaSUG 2025 Methodology for AI-driven Outcome Prediction for Patients with Atrial Fibrillation After Transcatheter Aortic Valve Implantation (TAVI) Felix Just,
Krishna Padmanabhan,
Parth Jinger,
Amanda Borrow,
Rüdiger Smolnik,
Eva-Maria Fronk,
Neelam Yadav,
Sergei Krivtcov,
Stas Zakharkin,
Martin Unverdorben,
Asmir Vodencarevic		 source 6
PharmaSUG 2025 Super Learner for Predictive Modeling and Causal Analysis Honghe Zhao,
Clay Thompson,
Michael Lamm		 source 9
PharmaSUG 2025 Zero to Hero: The Making of a Comprehensive R Shiny Figures App Yi Guo,
Matthew Salzano,
Nicholas Sun,
Sean Healey		 source 30
SESUG 2024 Interactive Modeling with SAS Visual Statistics Danny R. Modlin source 28
SESUG 2024 Unveiling the Diagnostic Potential of Radiomics-Based PET and CT Imaging through Statistical and Machine Learning Techniques Mostafa Zahed,
Janet Kireta,
Victor Aderanti,
Maryam Skafyan		 source 21
WUSS 2024 The Current State of Teaching Biostatistics in Academia [presentation] Lida Gharibvand source 14
PharmaSUG CN 2024 Application of Natural Language Processing with R in clinical trials Feifeng Zhou,
Zhenchao Chen		 source 11
PHUSE US 2024 Beyond Outliers: Unsupervised Anomaly Detection in Clinical Trial Data Helena Yaben,
Kian Norouzi		 source
presentation source		 9
PHUSE US 2024 Mining Historical Data and Epidemiological Evidence for Real-World Studies Shubhranshu Dutta source
poster source		 13
PHUSE US 2024 Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data Kostiantyn Drach,
Sergey Glushakov		 source
presentation source		 12
WUSS 2023 Leveraging Logistic Regression and Bootstrap Sampling to Assess a Decision Threshold in Classification Predictive Analytics Colleen E. McGahan source 15
WUSS 2023 What's next after model comparison? Model selection and model averaging in SAS Chong Ho Yu,
Charlene Yang		 source 14
SESUG 2023 %modelselect: A macro for automatically selecting statistical models based on fit statistics William B. Smith source 7
PHUSE EU 2022 Devices and Decentralised Clinical Trials Using a Storage and Advanced Analytics Platform for the Clinical Data Environment (ALYCE) [presentation] Stephan Cichos,
Anja Thiede		 source
video		 23
SAS Explore 2022 Software Workshop: Automated Large-Scale Forecasting [video] Chip Wells source
youtube
PharmaSUG 2022 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Matthew T. Slaughter,
Isaiah Lankham		 source 22
PHUSE EU 2021 Best Practices: Analysing Classical Clinical Trial Data with ML Methods [presentation] Uwe Pritzsche source
video		 10
PHUSE US 2021 Table-based Data Visualizations Using R [presentation] Matthew Kumar source
video		 26
PharmaSUG 2021 Build a model: Introducing a methodology to develop multiple regression models using R in oncology trial analyses Girish Kankipati,
Jai Deep Mittapalli		 source 20
PharmaSUG 2021 Mitigating Multicollinearity: Using Regulation Techniques to Minimize the Effect of Collinearity Deanna Naomi Schreiber-Gregory source 23
SGF 2021 A Framework for Simple and Efficient Bootstrap Validation in SAS, with Examples Isaiah Lankham,
Matthew T. Slaughter		 source 22
SGF 2021 Haste Makes Waste: Don't Ruin Your Reputation With Hasty Regression Steven C. Myers source
presentation
sas		 39
SGF 2021 Investing in Education Intelligently Sam Edison,
Rushya Puttam,
Akhil Emani,
Nithisha Katta		 source
presentation		 7
SESUG 2020 Outlier Detection and Treatment Ross Bettinger source 29
SGF 2020 %SURVEYCORRCOV Macro: Complex Survey Data Correlations for Multivariate Analysis and Model Building David R. Nelson,
Siew Wong-Jacobson		 source 20
SGF 2020 A Survey of Methods in Variable Selection and Penalized Regression Yingwei Wang source 17
SGF 2020 An Introduction to Multiple Time Series Analysis and the VARMAX Procedure Xilong Chen source 19
SGF 2020 Apply the Key Ideas from Andrew Ng's Machine Learning Certification (Coursera) in SAS Viya Petri Roine source 9
SGF 2020 Automation in SAS Visual Data Mining and Machine Learning Wendy Czika source 26
SGF 2020 Evolutionary Feature Selection for Machine Learning Nandini Rakala source 12
SGF 2020 Exploring Online Drug Reviews using Text Analytics, Sentiment Analysis, and Data Mining Models Thu Dinh,
Goutam Chakraborty		 source 23
SGF 2020 Forecasting Healthcare Statistics At the Cleveland Clinic Michael Bromley source 10
SGF 2020 Introducing the GAMSELECT Procedure for Generalized Additive Model Selection Michael Lamm,
Weijie Cai		 source 25
SGF 2020 Multinomial vs. Ordinal. Does model selection make a difference? for SAS Global Forum 2020 Corey Leadbeater source 15
SGF 2020 Recent Developments in Survival Analysis with SAS Software G. Gordon Brown source 9
SGF 2020 Scalable Cloud-Based Time Series Analysis and Forecasting Using Open Source Software Javier Delgado source 21
SGF 2020 Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software Isaiah Lankham,
Matthew T. Slaughter		 source
github		 15
SGF 2020 Using Analytics to Predict Tax Recovery and Prioritize Audits and Investigations in Canada Jason A. Oliver source 19
SGF 2020 Variable Selection Using Random Forests in SAS Denis Nyongesa source 11
SGF 2020 Winning Tactics for SAS Visual Data Mining and Machine Learning: Why it's the Only Skill You Need Chris St. Jeor source 12
PharmaSUG 2020 Detecting Side Effects and Evaluating the Effectiveness of Drugs from Customers' Online Reviews using Text Analytics, Sentiment Analysis and Machine Learning Models Thu Dinh,
Goutam Chakraborty		 source 22
PharmaSUG 2020 How to let Machine Learn Clinical Data Review as it can Support Reshaping the Future of Clinical Data Cleaning Process Mirai Kikawa,
Yuichi Nakajima		 source 9
PHUSE US 2020 Good Cooperation Between SAS and R for Regulatory Submission Jingyuan Chen source
presentation source		 8
PHUSE EU 2019 Symbolic Regression: Survival of the Best Fit Hector Leitch source
presentation source		 9
SESUG 2019 Advanced Statistical Modeling within Machine Learning and Big Data Analytics Niloofar Ramezani,
George Mason		 source 16
SESUG 2019 Validating Classification and Recidivism in Corrections Through Outcome Analytics Alan R. Mann source 34
SCSUG 2019 Building a Neural Network Scoring Model with JMP and Microsoft SQL Server to predict Aggregate Motor Vehicle Driver Alcohol Impairment David Whitchurch source 10
MWSUG 2019 Detecting Side Effects and Evaluating Effectiveness of Drugs from Customers' Online Reviews using Text Analytics and Data Mining Models Thu Dinh 23
MWSUG 2019 Survival Tips for Survival Analysis Xiaoting Wu 13
WUSS 2019 A strategy for speed Dating YuTing Tian source 21
WUSS 2019 Ordinal vs. Multinomial: Does Model Selection Make a Difference? Corey Leadbeater source 14
WUSS 2019 Polynomial Regression for Modeling Curvilinear Data: A Biological Example Elisha Johnston source 20
WUSS 2019 Variable Selection Using Random Forests in SAS Denis Nyongesa source 11
PharmaSUG 2019 An Introduction to the Process of Improving a Neural Network YuTing Tian source 22
PharmaSUG 2019 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory,
Karlen Bader		 source 23
SGF 2019 A Comparison of Algorithmic and Epidemiologic Approaches for Modeling Predictors of Type II Diabetes Audrey Whittle source 8
SGF 2019 An Introduction to the Process of Improving a Neural Network YuTing Tian source 22
SGF 2019 Creating a comprehensive risk-aware business culture using SAS Risk Management products Kelly Potter source 9
SGF 2019 Exploring the Intentions of Entering Entrepreneurship for SAS Global Forum 2019 Surabhi Arya,
Prashant Gour,
Dhruv Sharma,
Jeroen Vanheeringen		 source 7
SGF 2019 Introducing the MBC Procedure for Model-Based Clustering David Kessler source 21
SGF 2019 Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software David Richard Cutler source 21
SGF 2019 Playing Favorites: Our Top 10 Model Studio Features in SAS Visual Data Mining and Machine Learning Wendy Czika,
Peter Gerakios		 source 16
SGF 2019 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory source 23
SGF 2019 SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning Hand Shake Jagruti Kanjia,
Dominique Latour,
Holly Sweeney		 source 35
SGF 2019 Screening, Transforming, and Fitting Predictors for the Cumulative Logit Model Bruce S. Lund source 21
SGF 2019 Ten Underused Features That Improve Your SAS Forecast Server Workflow Evan Anderson,
Michael J. Leonard		 source 18
SGF 2019 The Aftermath What Happens after you Deploy Your Models and Decisions David R. Duling source 18
SGF 2019 The Relationship Between Social Determinants of Health and Healthcare Utilization Ryan C. Butterfield,
Melissa Gottschalk,
Laura Soloway,
Paul A. LaBrec		 source 16
SGF 2019 Unsupervised Learning for Anomaly Detection in Securities Data Laura Rudolphi,
Chris A. Robinson		 source 7
SGF 2019 Using SAS Forecast Server and the SASEFRED Engine to Enhance Your Forecast Catherine LaChapelle source 7
PHUSE US 2019 Maximizing the Use of Patient Data Warehouse via Visual Analytics Sandy Lei,
Simon Alex		 source
presentation source		 7
SCSUG 2018 Machine Learning in Litigation Prediction - Workers' Compensation Litigation Propensity Predictive Model Mei Najim source 17
SESUG 2018 A Quick Bite on SAS Studio Custom Tasks Michael Kola source 29
SESUG 2018 ANALYSIS OF FACTORS INFLUENCING DROPOUTS IN SOPHOMORE ENROLLMENT Apoorva Chandrasekaran source 14
SESUG 2018 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory,
Henry M. Jackson		 source 23
MWSUG 2018 An Introduction to the Process of Improving a Neural Network YuTing Tian 22
MWSUG 2018 Genocide Modeling - Historical Risk Factors and Odds Ratios David J. Corliss 5
MWSUG 2018 Regularization Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory,
Karlen Bader		 23
MWSUG 2018 Screening, Transforming, and Fitting Predictors for Cumulative Logit Model Bruce S. Lund 22
WUSS 2018 Agreeing to Disagree: Using SAS to Make Reasoned Decisions When Information Criteria Select Different Models Wendy Christensen source 10
WUSS 2018 Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets Deanna Naomi Schreiber-Gregory source 23
PHUSE US 2018 Feature Selection for Predictive Modelling - A Needle in a Haystack Problem Sudipta Basu source
presentation source		 6
PharmaSUG 2018 Build Models without Code with the new SAS Visual Interface Jim Box source 12
SGF 2018 !WRONG PAPER FIEL - WAITING for FIX! Nikki M. Carroll source 9
SGF 2018 Application of Propensity Score Models in Observational Studies Nikki M. Carroll source 9
SGF 2018 Bayesian Networks for Causal Analysis John Amrhein,
Fei Wang		 source 11
SGF 2018 Claim Analytics: A Litigation Prediction Case Study Mei Najim source 18
SGF 2018 Do You Need to Put Your Data on a Diet? Howard Plemmons Jr.,
Priya Sharma		 source 7
SGF 2018 Examining the Drivers of Hospital Re-admission of Type-2 Diabetic patients Shashank Reddy Gudipati source 9
SGF 2018 Extending the Capability of SAS Forecast Studio to Enable Custom Exception Handling Divya Guru Rajan source 7
SGF 2018 Improved Campaigning at Africa's Largest Pay TV Service Provider with SAS Marketing Optimization Jean De Villiers source 16
SGF 2018 Location Matters: Evidence from Spatial Econometric Analysis of Opioid Prescription Rates Jonathan Mccopp source 21
SGF 2018 Model Selection Using Information Criteria (Made Easy in SAS ) Wendy Christensen source 12
SGF 2018 Model Selection with Higher-Order Interactions in SAS MIXED and GLIMMIX Procedures [presentation] Yin Zhang,
Nanhua Zhang		 source 11
SGF 2018 Navigating the Analytics Life Cycle with SAS Visual Data Mining and Machine Learning Susan Haller,
Brett Wujek		 source 29
SGF 2018 Penalized Variable Selection and Quantile Regression in SAS : An Overview Russell M. Lavery source 28
SGF 2018 Regime-Switching Models: Capturing Structural Changes in Time Series Xilong Chen,
Ji Shen		 source 14
SGF 2018 SAS Visual Forecasting: A Cloud-Based Time Series Analysis and Forecasting System Jerzy M. Brzezicki,
Joseph H. Katz		 source 21
SGF 2018 Scalable Cloud-Based Time Series Analysis and Forecasting Thiago Quirino,
Michael J. Leonard,
Ed Blair		 source 22
SGF 2018 Top 10 Tips for SAS Enterprise Miner Based on 20 Years' Experience Melodie Rush source 21
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 Backward Variable Selection for Logistic Regression Based on Percentage Change in Odds Ratio Evan Kwiatkowski source 6
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 Logistic Model Selection with SAS PROC's LOGISTIC, HPLOGISTIC, HPGENSELECT Bruce S. Lund 18
SGF 2017 A Hadoop Journey along the SAS Road Sumit Sukhwani,
Krutharth Peravalli,
Amit Gautam		 source 8
SGF 2017 Building Bayesian Network Classifiers Using the HPBNET Procedure Ye Liu,
Weihua Shi,
Wendy Czika		 source 19
SGF 2017 Construction of a Disease Network and a Prediction Model for Dementia Jinwoo Cho source 14
SGF 2017 Multivariate Time Series: Recent Additions to the VARMAX Procedure Xilong Chen,
Stefanos Kechagias		 source
zip		 14
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
SGF 2017 Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data Josephine Akosa source 12
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 Random Forests with Approximate Bayesian Model Averaging Tiny Du Toit,
Andre De Waal		 source 11
SGF 2017 Using a Population Average Model to Investigate the Success of a Customer Retention Strategy Colleen E. McGahan source 19
SGF 2017 What Statisticians Should Know about Machine Learning David Richard Cutler source 13
SCSUG 2016 Determining the functionality of Water Pumps in Tanzania Using SAS EM and VA Indra Kiran Chowdavarapu,
Vivek Damodaran Manikandan		 source 9
SESUG 2016 Know your Interest Rate Anirban Chakraborty,
Goutam Chakraborty		 source 16
SESUG 2016 SAS Macro for Automated Model Selection Involving PROC GLIMMIX and PROC MIXED Fan Pan,
Jin Liu		 source 9
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 Animated Guide: Penalized variable selection techniques in SAS and Quantile Regression Russell M. Lavery,
Peter Flom		 24
MWSUG 2016 Property and Casualty Insurance Predictive Analytics in SAS Mei Najim 17
MWSUG 2016 Use Multi-Stage Model to Target the Most Valuable Customers Chao Xu,
Jing Ren,
Hongying Yang		 10
WUSS 2016 An Animated Guide: Incremental Response Modeling in Enterprise Miner Russell M. Lavery source 19
SGF 2016 A Property and Casualty Insurance Predictive Modeling Process in SAS Mei Najim source 14
SGF 2016 Application of Data Mining Techniques in Improving Breast Cancer Diagnosis Josephine Akosa source
presentation		 10
SGF 2016 Application of Gradient Boosting through SAS Enterprise Miner to Classify Human Activities Minh Pham,
Mary Ruppert-Stroescu,
Mostakim Tanjil		 source
presentation		 8
SGF 2016 Case Study Using SAS Forecast Studio: Daily Taxi Data from New York City [presentation] Simon Sheather source 47
SGF 2016 Ensemble Modeling: Recent Advances and Applications Wendy Czika,
Ye Liu		 source 20
SGF 2016 Finding and Evaluating Multiple Candidate Models for Logistic Regression Bruce S. Lund source
zip		 17
SGF 2016 Latent Class Analysis Using the LCA Procedure Patricia A. Berglund source 18
SGF 2016 Predicting If Mental Health Facilities Will Offer Free Treatment to Patients Who Cannot Afford It Ram Poudel,
Lynn Xiang		 source 11
SGF 2016 Predicting Occurrence Rate of Systemic Lupus Erythematosus and Rheumatoid Arthritis in Pregnant Women Ravikrishna Vijaya Kumar,
Shrie Raam Sathyanarayanan		 source 15
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 Real-Time Predictive Modeling of Key Quality Characteristics Using Regularized Regression: SAS Procedures GLMSELECT and LASSO Jon M. Lindenauer source 10
SGF 2016 Reducing and Screening Redundant Variables in Logistic Regression Models Using SAS/STAT Software and SAS Enterprise Miner Xinghe Lu source 10
MWSUG 2015 A Property & Casualty Insurance Predictive Modeling Process in SAS Mei Najim 14
MWSUG 2015 Fitting and Evaluating Logistic Regression Models Bruce S. Lund 26
MWSUG 2015 Get the highest bangs for your marketing bucks using Incremental Response Models in SAS Enterprise Miner Delali Agbenyegah 8
SESUG 2015 A SAS Macro to Investigate Statistical Power in Meta-analysis Jin Liu,
Fan Pan		 source 5
WUSS 2015 Introduction to SAS Quantile Regression (PROC QuantSelect and PROC QuantReg ) Peter L. Flom source 25
PharmaSUG CN 2015 Why Statistical Analysis Plan (SAP) should be comprehensive. Riddhi Merchant,
Ranjith Prayankotveettil		 6
SGF 2015 Addressing AML Regulatory Pressures by Creating Customer Risk Rating Models with Ordinal Logistic Regression Edwin Rivera,
Jim West		 source 12
SGF 2015 Analyzing Parking Meter Transactions Using SAS Procedures Anders Milhøj source 12
SGF 2015 Count Series Forecasting Michael J. Leonard source 14
SGF 2015 Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante source 21
SGF 2015 Incorporating External Economic Scenarios into Your CCAR Stress Testing Routines Kenneth Sanford,
Christian Macaro		 source 10
SGF 2015 Lasso Regularization for Generalized Linear Models in Base SAS Using Cyclical Coordinate Descent Robert Feyerharm source 10
SGF 2015 Modelling Operational Risk Using Extreme Value Theory and Skew t-Copulas via Bayesian Inference Using SAS Betty Johanna Garzon Rozo source 20
SGF 2015 Multinomial Logistic Model for Long-Term Value Bruce S. Lund source 16
SGF 2015 Predicting Readmission of Diabetic Patients Using the High-Performance Support Vector Machine Algorithm of SAS Enterprise Miner 13.1 Hephzibah Munnangi,
Goutam Chakraborty		 source 10
SGF 2015 The %ic_mixed Macro: A SAS Macro to Produce Sorted Information Criteria (AIC and BIC) List for PROC MIXED for Model Selection Qinlei Huang source 16
SGF 2015 Understanding Characteristics of Insider Threats by Using Feature Extraction Ila Gokarn,
Clifton Phua		 source 14
SESUG 2014 Penalizing Your Models: An Overview of the Generalized Regression Platform Michael Crotty,
Clay Barker		 source 8
SESUG 2014 VIF Regression: A SAS Application to Feature Selection in Large Data Sets Ruiwen Zhang 5
MWSUG 2014 A SAS Macro Using Parallel Genetic Algorithm to Automate Variable Selection Jinqiao Li,
Yi Cao,
Yanping Shen		 19
MWSUG 2014 Analyzing Collection Effectiveness using Incremental Response Modeling Ryan Burton 7
MWSUG 2014 Prediction Improvement from Geostatistical Methodology in Regression Modeling For Census Tract Data Robert G. Downer 10
PharmaSUG 2014 %IC_LOGISTIC: A SAS Macro to Produce Sorted Information Criteria (AIC/BIC) List for PROC LOGISTIC for Model Selection Qinlei Huang,
Liang Zhu		 source 12
SGF 2014 A Stepwise Algorithm for Generalized Linear Mixed Models Nagaraj Neerchal,
Jorge Morel,
Xuang Huang,
Alain Moluh		 source 11
SGF 2014 An Ensemble Approach for Integrating Intuition and Models [presentation] Masoud Charkhabi,
Ling Zhu		 source 3
SGF 2014 An Overview of Machine Learning with SAS Enterprise Miner Patrick Hall,
Jared Dean,
Ilknur Kaynar Kabul,
Jorge Silva		 source
zip		 24
SGF 2014 Building Gamer Segmentation in the Credit Card Industry Using SAS Enterprise Guide Yang Ge source 10
SGF 2014 Ex-Ante Forecast Model Performance with Rolling Simulations Michael J. Leonard,
Ashwini Dixit,
Udo Sglavo		 source 18
SGF 2014 Graphs Useful for Variable Selection in Predictive Modeling Bob Moore source 9
SGF 2014 Model Variable Selection Using Bootstrap Decision Tree David J. Corliss source 7
SGF 2014 Text Mining Economic Topic Sentiment for Time Series Modeling Michael P. Dessauer,
Justin Kauhl		 source 14
SGF 2014 What's New in SAS Enterprise Miner 13.1 Jared Dean,
Jonathan Wexler		 source 16
WUSS 2013 An Algorithm for ARIMA Dynamic Regression Variable Selection Ross Bettinger source 14
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
MWSUG 2013 Reuse, Don't Reinvent: Extending Model Selection Using Recursive Macro Anca M Tilea,
Philip L Francis III		 5
NESUG 2013 Model Variable Selection Using Bootstrapped Decision Tree David J. Corliss 8
SGF 2013 %MIXED_FIT: A SAS Macro to Assess Model Fit and Adequacy for Two-Level Linear Models Bethany A. Bell,
Mihaela Ene,
Whitney Smiley		 source
pdf		 8
SGF 2013 Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples Irena Stijacic Cenzer,
W. John Boscardin,
Katharine Kirby,
Yinghui Miao		 source 12
SGF 2013 Leveraging Big Data Using SAS High-Performance Analytics Server Priya Sharma source 9
SGF 2013 Using Data Mining in Forecasting Problems Chip Wells,
Justin Kauhl,
Timothy Rey		 source 17
PHUSE EU 2012 The Logic and Logistics of Logistic Regression, Including New Features in SAS 9.2 Lawrence Rasouliyan source
presentation source		 16
SESUG 2012 %MIXED_FIT: A SAS Macro to Assess Model Fit and Adequacy for Two-Level Linear Models Mihaela Ene source 8
SESUG 2012 The Elephant in the Room: Hadoop & SAS Integration Greenplum, SAS & Hadoop Chris Stephens 23
MWSUG 2012 An Analysis of Diabetes Risk Factors Using Data Mining Approach Akkarapol Sa-ngasoongsong,
Jongsawas Chongwatpol		 11
MWSUG 2012 Combined Forecasts: What to Do When One Model Isn't Good Enough Ed Blair,
Michael J. Leonard,
Bruce Elsheimer		 14
MWSUG 2012 Model Selection Using Recursive Macro - Enhancements to R2 Selection in PROC REG Anca M Tilea,
Philip L Francis III,
Brenda W Gillespie,
Rajiv Saran		 10
WUSS 2012 Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples Irena Stijacic Cenzer,
Yinghui Miao,
Katharine Kirby,
W. John Boscardin		 source 9
SGF 2012 A New Age of Data Mining in the High-Performance World David R. Duling,
Wayne Thompson,
Jared Dean		 source 11
SGF 2012 Black Box PROCs: PROC LOGISTIC Discovered Isabel H. Perry source 7
SGF 2012 Combined Forecasts: What to Do When One Model Isn't Good Enough Ed Blair,
Michael J. Leonard,
Bruce Elsheimer		 source 14
SGF 2012 Developing a Predictive Model for Customer Trip Purpose to Be Integrated into Enterprise Strategy and Analytics Jonathan Levine source 16
SGF 2012 Making it Happen: Using SAS to Implement Business Intelligence and Analytics in Small and Midsize Organizations Richard R. Hallquist,
Dragos Coles,
Lorrie Gallego		 source 16
SGF 2012 Using SAS Enterprise BI and SAS Enterprise Miner to Reduce Student Attrition Matt T. Bogard,
Chris James,
Tuesdi Helbig,
Gina Huff		 source 10
SGF 2012 Variable Selection for Multivariate Cointegrated Time Series Prediction with PROC VARCLUS in SAS Enterprise Miner 7.1 Akkarapol Sa-ngasoongsong,
Satish T. Bukkapatnam		 source 12
SESUG 2011 An Exact Implicit Enumeration Algorithm for Variable Selection in Multiple Linear Regression Models Using Information Criteria Dennis J. Beal source 12
SESUG 2011 Eyes on the Road: A Methodology for Analyzing Complex Eye-Tracking Data Eye Tracking source 11
SESUG 2011 Proc MIXED - Right Options to get Right Output Shilpa C. Edupganti,
Sheetal Nisal		 source 9
WUSS 2011 Modeling Interactive Advertising on TiVo: Overcoming the Challenges of Collinearity, Efficiency and Practicality Rachel Poulsen source 10
MWSUG 2011 SAS Model Selection Macros for Complex Survey Data Using PROC SURVEYLOGISTIC/SURVEYREG Fang Wang,
Hee-Choon Shin		 27
MWSUG 2011 Using SAS to Create Sales Expectations for Everyday and Seasonal Products Lory Ellebracht,
Jamie Netherton,
Casey Gentry		 10
NESUG 2011 Feature Selection and Dimension Reduction Techniques in SAS Sassoon Kosian,
Varun Aggarwal		 6
NESUG 2011 Proc Mixed - Right Options to Get the Right Output Shilpa C. Edupganti,
Sheetal Nisal		 10
PharmaSUG 2011 Classification of frequent snoring from routine medical examinations using the NHANES Database Barry W. Row source 11
PharmaSUG 2011 Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterprise Miner Patricia B. Cerrito source 15
SGF 2011 BASE SAS Implementation of Information Theoretic Feature Selection for Neural Networks Martin B. Jetton source 13
SGF 2011 Extending SAS High-Performance Forecasting Using User-Defined Models and External Forecasts Udo Sglavo source 9
SGF 2011 Improving Customer Loyalty Program through Text Mining of Customers Comments Maheshwar R. Nareddy,
Goutam Chakraborty		 source 8
SGF 2011 Perl Regular Expression in SAS Macro Programming Yanwei Zhang source 7
SGF 2011 To Hash or Not To Hash! That Is the Question!! When it Comes to SQL!!! Mustaq Ahmad source 12
PHUSE EU 2010 Using Data and Text Mining to drive Innovation David Smith source
presentation source		 6
SESUG 2010 Developing a Model for Person Estimation in Puerto Rico for the 2010 Census Coverage Measurement Program Colt S. Viehdorfer source 12
SGF 2010 Analysis of Survival Data with Recurrent Events Using SAS Rena Jie Sun,
Daniel T. Cotton		 source 8
SGF 2010 Modeling the Severity of Random Events with the SAS/ETS SEVERITY Procedure Mahesh V. Joshi source 20
SGF 2010 Rapid Predictive Modeling for Customer Intelligence Wayne Thompson,
David R. Duling		 source 12
MWSUG 2009 Mixed Model Selection George C. J. Fernandez 19
PNWSUG 2009 Building Multiple Linear Regression Models - Food for Thought Colleen E. McGahan,
Mei Chong		 11
WUSS 2009 Optimal Model Selection By a Genetic Algorithm Using SAS Jerry Tsai source 13
PharmaSUG 2009 Illustrative Logistic Regression Examples using PROC LOGISTIC: New Features in SAS/STAT 9.2 Robert G. Downer,
Patrick J. Richardson		 11
SGF 2009 Applications of the GLMSELECT Procedure for Megamodel Selection Robert A. Cohen source 25
SGF 2009 The Next Generation: SAS Enterprise Miner 6.1 David R. Duling,
Wayne Thompson		 source 10
WUSS 2008 User-friendly SAS Macro Application for performing all possible mixed model selection - An update George C. J. Fernandez source 19
SESUG 2008 SAS Code for Variable Selection in Multiple Linear Regression Models Using Information Criteria Methods with Explicit Enumeration for a Large Number of Independent Regressors Dennis J. Beal,
Oak Ridge		 source 9
MWSUG 2008 Classification of Breast Cancer Cells Using JMP Marie Gaudard 34
PharmaSUG 2008 From Soup to Nuts: Practices in Data Management for Analytical Performance David R. Duling 25
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
SGF 2008 From Soup to Nuts: Practices in Data Management for Analytical Performance David R. Duling,
Howard Plemmons Jr.,
Nancy A. Rausch		 source 24
SGF 2008 How to Use SAS to Fit Multiple Logistic Regression Models Anpalaki J. Ragavan source 13
SGF 2008 In Search for a Golden Algorithm Tamara S. Slipchenko,
Candice Bowman,
Catherine Sugar,
Allen L. Gifford		 source 9
SGF 2008 Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry Glendon Cross,
Wayne Thompson		 source 14
SGF 2008 Using SAS Enterprise Miner to Prescribe a Pre-Screen Mailing Rex Pruitt source 6
NESUG 2007 Implementing Multiple Imputation in an Automatic Variable Selection Scenario Krishna Mehta,
Mayank Rustagi,
Saurabh Kohli,
Siddharth Tiwari		 4
NESUG 2007 Using Macro and ODS to Overcome Limitations of SAS Procedures Wei (Lisa) Lin,
Jing Su		 7
NESUG 2007 Variable Selection and Variable Transformations in SAS Enterprise Miner 5.2 Kattamuri S. Sarma 12
SESUG 2007 Data Mining and Investigating Treatment Options of Osteomyelitis with MRSA Hamed Zahedi source 9
SESUG 2007 Information Criteria Methods in SAS for Multiple Linear Regression Models Dennis J. Beal source 10
MWSUG 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga 20
WUSS 2007 Case Study: Development of an HIV casefinding algorithm with SAS Enterprise Miner Tamara Slipchenko,
Candice Bowman,
Yuexin (Cindy) Chen,
Catherine Sugar,
Allen L. Gifford		 source 9
WUSS 2007 Data Mining Using SAS Enterprise Miner Randall Matignon source 10
WUSS 2007 Recreating the SELECTION=SCORE Model Specification with the BEST=n Effect Selection Option for PROC SURVEYLOGISTIC Sara R. Adams,
David R. Mink,
Dave P. Miller		 source 8
PHUSE EU 2007 Data mining in the clinical research environment David Smith source 7
SCSUG 2007 A SAS Macro for Hosmer and Lemeshow's Purposeful Selection Model Building Algorithm: Description and Performance David Williams,
Zoran Bursac,
David K. Hosmer		 6
SCSUG 2007 Deploying Predictive Models in the Newspaper Industry April Wilson,
Kuao-Tien Chen		 23
PharmaSUG 2007 A Review of Propensity Score Application in Healthcare Outcome and Epidemiology Guiping Yang 24
PharmaSUG 2007 Effects of Multicollinearity in All Possible Mixed Model Selection George C. J. Fernandez 18
SGF 2007 A Purposeful Selection of Variables Macro for Logistic Regression Zoran Gauss,
C. Heath Williams,
David K. Hosmer,
David W. Bursac		 source 5
SGF 2007 Identifying and Overcoming Common Data Mining Mistakes Doug Wielenga source 20
SGF 2007 Model Selection in PROC MIXED-A User-Friendly SAS Macro Application George C. J. Fernandez source 20
SGF 2007 Predicting Software Maintenance Outcomes Using Data Mining and Text Mining Uzma Raja source 9
SGF 2007 SAS Macros for Automated Model Selection When the Response Variable Is Nominal-Needs and Solutions Involving PROC CATMOD Bonnie E. Williams,
Todd Kegan		 source 12
SGF 2007 Variable Screening for Multinomial Logistic Regression on Very Large Data Sets as Applied to Direct Response Modeling John A. Cherrie source 12
PNWSUG 2006 You Can't Stop Statistics: SAS/STAT Software Keeps Rolling Along Maura E. Stokes,
Robert N. Rodriguez		 32
MWSUG 2006 All Possible Model Selection in Proc Mixed - A SAS macro application George C. J. Fernandez 19
WUSS 2006 Mixed Model Selection via the Information-Theoretic Approch using SAS Jesse Albert Canchola,
Torsten B. Neilands		 source 7
WUSS 2006 Model Selection Methods in PROC MIXED George C. J. Fernandez source 19
WUSS 2006 The Logic and Logistics of Logistic Regression Lawrence Rasouliyan,
Dave P. Miller		 source 14
NESUG 2006 Variable Selection in the Credit Card Industry Alec Cheng,
Moez Hababo,
Ray Falk		 5
NESUG 2006 You Can't Stop Statistics Maura E. Stokes,
Robert N. Rodriguez		 32
SUGI 2006 Application of Proc Discrim and Proc Logistic in Credit Risk Modeling Jin Li source 9
SUGI 2006 Comparison of Data Preparation Methods for Use in Model Development with SAS Enterprise Miner Charles Manahan source 10
SUGI 2006 Introducing the GLMSELECT Procedure for Model Selection Robert A. Cohen source 18
SUGI 2006 You Can't Stop Statistics Maura E. Stokes,
Robert N. Rodriguez		 source 32
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
MWSUG 2005 Mining Transactional And Time Series Data Michael J. Leonard 26
SUGI 2005 Automating Predictive Analysis to Predict Medicare Fraud Christine L. Warner source 9
SUGI 2005 Combining Decision Trees with Regression in Predictive Modeling with SAS Enterprise Miner Kattamuri S. Sarma source 10
SUGI 2005 Introducing SAS Forecast Studio Michael J. Leonard,
Brenda Wolfe,
Paddy Fahey		 source 16
SUGI 2005 Mining Transactional And Time Series Data Michael J. Leonard,
Brenda Wolfe		 source 26
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 2005 On GSForward-The Experimental Variable Selection Node in SAS Enterprise Miner 5.1 Leonardo Auslender source 6
PharmaSUG 2004 Making Model Selection in Linear Mixed-Effects Models An Easy Process: A SAS Macro Procedure Henry Cheng,
Yuli Xie,
Elizabeth Reinbolt		 4
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 2004 Fitting Longitudinal Mixed Effect Logistic Regression Models with the NLMIXED Procedure Peter H. Van Ness,
John R. O'Leary,
Amy L. Byers,
Terri R. Fried,
Joel A. Dubin		 source 7
WUSS 2003 A SAS Macro Solution for Scoring Test Data with Output Features and Parameters from Logistic Regression Model Training Joseph Blue source 5
WUSS 2003 Advanced Analytics with Enterprise Guide Catherine Truxillo,
Stephen McDaniel		 source 9
WUSS 2003 Multiple Linear Regression Analysis of Percent of Students Taking the ACTs in Nevada High Schools Ramya R. Fernandez,
George C. J. Fernandez		 source 6
WUSS 2003 Regression Diagnostic Plots George C. J. Fernandez source 9
NESUG 2003 Backwards Manual Selection (%bms): A Macro for Selecting Logistic Regression Models to Address Epidemiological Research Questions Janice Hegewald,
Anette Pfahlberg,
Wolfgang Uter		 5
SEUGI 2003 Analytical CRM at Swisscom Fixnet - The data warehouse concept, mode development with Enterprise Miner and implementation [presentation] Miltiadis Sarakinos source 22
SEUGI 2003 Data Mining for Predicting Customer Satisfaction [presentation] Francesco Vivarelli source 35
SEUGI 2003 Developing an Optimal Product Offering Strategy and Maximizing Overall Profitablility of Current Clients [presentation] Tamer Keshi source 13
SEUGI 2003 SAS Demand Intelligence [presentation] Udo Sglavo source 27
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
NESUG 2002 One More Time About R2 Measures of Fit in Logistic Regression Ernest S. Shtatland,
Ken Kleinman,
Emily M. Cain		 6
SEUGI 2002 Customer management from a data warehouse - A Journey not a Destination [presentation] Tal Shlasky source 33
SEUGI 2002 Payment Behavior Analysis and Prediction Project at a Telco Company [presentation] Osge Neslihan Cayci source 21
SUGI 2002 To Neural or Not to Neural? - This Is the Question Leon L. Fedenczuk source 6
SUGI 2002 Tree-based Models: Identification of Influential Factors under Condition of Instability Pavel Brusilovskiy,
Yillian Yuan		 source 6
SUGI 2002 Using SAS Enterprise Miner for Data Quality Monitoring in the Veterans Health Administration's External Peer Review Program James H. Forsythe source 5
VIEWS 2001 The Use of Enterprise Miner with Large Volumes of Data, for Forecasting in an Automated Batch Process [presentation] Matthew Glasson,
Justine Eastman		 17
SCSUG 2001 Ideas on Variable Selection and Alternative Links in Procedure CATMOD Kimberly DeJarnatt,
James E. Dunn		 4
SEUGI 2001 Fraud Detection in Healthcare - MBF's successful approach through SAS South Pacific Data Mining Discovery Centre [presentation] Jolie Reichel source 14
SEUGI 2001 The Use of Enterprise Miner with Large Volumes of Data, for Forecasting in an Automated Batch Process [presentation] Justine Eastman source 12
SUGI 2001 Partitioned Mahalanobis D^2 to Improve GIS Classification Lynette K. Duncan,
James E. Dunn		 source 6
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
NESUG 2000 How to Be a Bayesian in SAS: Model Selection Uncertainty in PROC LOGISTIC and PROC GENMOD Ernest S. Shtatland 9
NESUG 2000 Point & Click Forecasting with the Time Series Forecasting System Charles B. Hallahan 9
WUSS 2000 An Overview of remedial tools for collinearity in SAS Chong Ho Yu source 6
SEUGI 2000 Enterprise Mineer vs Underlying SAS Code: A Performance Comparison [presentation] Usama Hasan source 11
SEUGI 2000 Genetic Data Analysis with Enterprise Mineer [presentation] John C. Brocklebank,
Wayne Thompson		 source 24
SEUGI 2000 SAS Software for the Analysis of Epidemiologic Data [presentation] Hans-Peter Altenburg source 39
SUGI 2000 SAS Programs to Get the Most from X-12-ARIMA's Modeling and Seasonal Adjustment Diagnostics Catherine C. Hood source 6
SUGI 2000 The Time Series Forecasting System Charles B. Hallahan source 9
SEUGI 1999 Credit risk analysis: an empirical approach [presentation] Angela Ancona,
Emiliano Laruccia,
Flavio Addolorato		 source 22
SEUGI 1999 Integrating management reporting with SAS Software: balanced scorecard, data mining and OLAP Analysis for an insurance company [presentation] Patrick Imark source 30
SUGI 1999 The Time Series Forecasting System Charles B. Hallahan,
Renee Samy		 source 9
WUSS 1998 Investigating the Performance of Model Selection Information Criteria Using SAS/IML and SAS/MACRO Long Ngo source 6
WUSS 1998 Multiple Regression and Model Selection on Developing a Computer Achievement Model Leping Liu,
George C. J. Fernandez		 source 6
WUSS 1998 UNIX Explorer: Pretty Remote Computing Mark T. Stetz source 9
NESUG 1998 Risk Evaluation after Heart Valve Replacement by PROC PHREG Choa Chen 7
SESUG 1998 An Applicatio.n of Cluster Analysis in the Financial Services Industry Satrsh Nargundkar,
Strategic Decision		 6
SUGI 1998 Finding the Solution to Data Mining: A Map of the Features and Components of SAS Enterprise Miner Software Table of Contents John C. Brocklebank,
Mark Brown		 source 24
SUGI 1998 Mining the Data Mart: A Case Study with Stock Market Data James A. Cox,
Tonya L. Etchison		 source 11
SUGI 1998 Multivariate Analysis Using the MIXED Procedure S. Paul Wright source 5
WUSS 1997 Analysis of Designed Experiments Using SAS PROC GENMOD Douglas C. Montgomery,
Connie M. Borror,
Sharon L. Lewis		 source 6
SUGI 1997 Model Selection in Linear Mixed Effects Models Using SAS PROC MIXED Long Ngo,
Richard Brand		 source 6
MWSUG 1995 Developing SAS/AF Applications for Reviewing Clinical Care Carl R. Haske 5
SEUGI 1995 Model Building for the Cox Proportional Hazards Model with the SAS System Richard Vonk source 12
SUGI 1995 An Interactive Program of the GEE Method for the Analysis of Longitudinal Data Masakazu Andoh,
Tohru Uwoi		 source 6
SUGI 1995 Bootstrap Cox Regression Using SAS Procedures Chikuma Hamada,
Junji Kishimoto		 source 5
SUGI 1995 Introducing the Market Research Analysis Application Wayne E. Watson source 8
SUGI 1995 Using SAS/AF Software and Frame Entry to Access Data Carl R. Haske source 5
SEUGI 1994 Practical Aspects of Modelling Techniques in Logistic Regression Procedures of the SAS System Josef Hogel source 6
SUGI 1994 Forecasting Operational Indices Using SAS/ETS Software Laura McCarty source 5
SUGI 1994 Tracking Software for Longitudinal Studies at the National Center for Health Statistics Sandra T. Rothwell,
Aileen D. Bennett		 source 5
SEUGI 1993 Variable Selection Techniques Implemented in Procedures of SAS Software Olaf Gefeller source 9
SUGI 1993 Developing an Interactive Method of Displaying Pavement Management Information Bruce Dietrich source 8
SUGI 1993 Forecasting Methods Mark Little source 13
SUGI 1993 MONOSEL: A SAS Macro for Model Selection in Linear Regression Analysis Victor Kamensky,
Ilya Muchnik		 source 6
SESUG 1993 Developing an Interactive Method of Displaying Pavement Management Information Using SAS/AF and PROC GMAP Bruce Dietrich 7
SEUGI 1992 Automatic Forecasting Menu System Joachim Hefner source 11
SUGI 1992 Ad Hoc Access to Diverse Databases for the Computer Illiterate Eric Larson source 8
SUGI 1992 Diagnostics for Model Selection in Nonlinear Regression Based on the Concept of Closeness to Linearity Using SAS/IML Software Suzette Blanchard,
Martin S. Levy		 source 3
SUGI 1992 Model Selection for Uncensored Lifetime Data Using SAS/IML Software Sutaip L. C. Saw source 5
SCSUG 1991 A Variable Selection Algorithm for Quadratic Discriminant Analysis Samuel L. Seaman,
Dean M. Young		 4
SUGI 1991 A SAS Program for Seasonal Kendall Trend Analysis of Monthly Water Quality Data Sungsue Rheem,
Golde I. Holtzman		 source 6
SUGI 1991 Managing Multicollinearity with the SAS System Rudolf J. Freund source 9
NESUG 1990 Efficient SAS Software Programming: A New Synthesis Jean Hardy 8
SEUGI 1990 Statistical Simulation with the SAS System Gerd Antes source 7
SUGI 1989 HBS-QUERY: Painless and Efficient Database Access Using the SAS System, REXX and XEDIT under CMS Barry Feldman source 6
SUGI 1989 The Use of SAS/IML Software in Implementing the Coherence Property for Discriminate Analysis Variables Joyce Bernstein,
William R. Fehlner		 source 5
SUGI 1988 Regression and the SAS System: An Overview Rudolf J. Freund source 9
SUGI 1988 Using SAS/STAT Software's REG Procedure to Develop Sales Tax Audit Selection Models Kirk L. Johnson,
Richard W. Kulp		 source 6
SUGI 1987 A SAS/ETS Software Forecasting and Inventory Planning System David Brauer,
Patrick Cardamone		 source 6
SUGI 1987 Building a Clinical Data Review System Using SAS/AF Software James F. Sattler source 7
SUGI 1987 Creating a User-Friendly PROC FSCALC Application Patti Brideson source 6
SUGI 1987 Preparing and Presenting Management Reports using SAS/AF Software Darius S. Baer source 6
SUGI 1986 Variable Selection and Case Analysis in Regression Gerald R. Hobbs source 6
SEUGI 1985 Test Procedures for Model Selection and Model Validation Wil Velding source 23
SUGI 1982 Variable Selection for Categorical Data Using SAS Erik J. Bergstralh,
Susan McGorray		 source 6

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