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199 papers and 13 presentations; 199 titles.

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Conference × Year × Title × Authors Extras Pages
WUSS 2026 Getting Started with Bayesian Analysis [presentation] Danny R. Modlin source 14
WUSS 2026 Stress Testing Your Findings: E-Values and Quantitative Bias Analysis in SAS Mehrnaz Siavoshi source 13
PharmaSUG CN 2026 An Integrated, AI-Augmented R Shiny Platform for Oncology Clinical Trial Decision Intelligence: From CDISC Data to Go/No-Go in One Unified Hub Zhiping Yan source 28
PHUSE US 2026 Robust Causal Inference in Real-World Evidence Studies with Double Machine Learning Yilun Huang,
Jing Dai		 source
presentation source		 5
PHUSE APAC 2026 Faster Patient Recruitment in Clinical Trials through Simulations Keerthana Udupa source
presentation source		 7
MWSUG 2025 PROC BGLIMM: The Smooth Transition to Bayesian Analysis Danny R. Modlin source 57
SESUG 2025 Introduction to Data Simulation Jason Brinkley 10
SESUG 2025 PROC BGLIMM: The Smooth Transition to Bayesian Analysis Danny R. Modlin 57
WUSS 2025 PROC BGLIMM: The Smooth Transition to Bayesian Analysis [presentation] Danny R. Modlin source 57
PharmaSUG 2025 Calculating exact posterior probabilities and credible intervals from Bayesian borrowing robust mixture priors for binary, count and continuous outcomes in R and SAS Darren Scott,
Armando Turchetta		 source 20
PharmaSUG 2025 Unleashing Oncology Revenue Insights: Advanced Forecasting Frameworks in Action Naquan Ishman,
Dave Kestner		 source 10
PHUSE US 2025 Revolutionising Cancer Trials with Adaptive Designs and Bayesian Analysis Navneet Jha source
presentation source		 11
MWSUG 2024 What if Proc Power Can't Help? Calculating Power via Simulation, Example with Count Regression Brandy R. Sinco,
Joceline Vu,
Pasithorn Suwanabol		 source 16
SESUG 2024 Deduping Time-Varying Redundant Transactions in Commercial Lending Patralekha Bhattacharya source 7
WUSS 2024 Integration of an R Language Function Within SAS to Perform Conditional Multivariate Saddlepoint Approximation Christopher Johnson source 8
WUSS 2024 Matrices and More: An Overview of the SAS/IML Language [presentation] Rick Wicklin source 46
WUSS 2024 Ten Tips for Simulating Data with SAS [presentation] Rick Wicklin source 34
PharmaSUG 2024 Dealing with Missing Data: Practical Implementation in SAS and R Isabella Y. Wang,
Lauren George,
Jin Xie		 source 20
SGF 2020 Best Practices for Converting SAS Code to Leverage CAS Steven Sober,
Brian Kinnebrew		 source 11
SGF 2020 Fitting Statistical Models with PROC MCMC Mark Ghamsary source 26
SGF 2020 Forecasting Healthcare Statistics At the Cleveland Clinic Michael Bromley source 10
SGF 2020 Modeling of Between - and Within-Subject Variances using Mixed Effects Location Scale (MELS) Models Qianheng Ma,
Donald Hedeker		 source 14
SGF 2020 Pricing Life Insurance Contracts with Early Exercise Features Using Neural Networks Helgard Raubenheimer source 9
SGF 2020 Risk Modeling on the Fast-Track: Pythonistas (and Others) Harness the Power of the SAS Risk Engine Dave Stonehouse source 9
SGF 2020 Write Custom Parallel Programs by Using the iml Action Rick Wicklin,
Arash Dehghan Banadaki		 source 18
SESUG 2019 Time Series Forecasting using PROC MCMC SESUG 2019 Murali Sastry,
Tyler Hicks		 source 16
WUSS 2019 Bayesian Nonparametric Clustering in SAS Hend Aljobaily source 9
WUSS 2019 Implementing the Hamiltonian Monte Carlo Sampling algorithm in PROC MCMC Elsa Vazquez Arreola source 10
SGF 2019 Commodities Risk Easy Button: Configure Advanced Risk Analytics from Scratch in 20 Minutes or Less Albert Hopping,
Eric Carriere,
Michael Busby,
Christine Jackson		 source 13
SGF 2019 Pseudo-Random Numbers in SAS Model Implementation Platform Joel Dood source 11
SGF 2019 The Categorical Might of PROC FREQ Jinson J. Erinjeri,
Saritha Bathi		 source 18
SESUG 2018 Introduction to Data Simulation Jason Brinkley source 9
SESUG 2018 The Categorical Might of PROC FREQ Jinson J. Erinjeri,
Saritha Bathi		 source 18
SGF 2018 Forecasting the Value of Fine Wines Joseph Breeden source 9
SGF 2018 Getting Started with Bayesian Analysis [presentation] Danny R. Modlin source 14
SGF 2018 Monte Carlo K-Means Clustering [presentation] Donald Wedding source 227
SGF 2018 SAS Fraud Framework and MCMC in Government Estimation of Improper Payments of Social Benefits Rodrigo Hildebrand,
Aloisio Dourado Neto		 source 10
SGF 2018 Tips and Techniques for Using the Random-Number Generators in SAS Rick Wicklin,
Warren Sarle		 source 14
SESUG 2017 Missing Data and Complex Sample Surveys Using SAS: The Impact of Listwise Deletion vs. Multiple Imputation Methods on Point and Interval Estimates when Data are MCAR, MAR, and MNAR Listwise Deletion source 26
SGF 2017 SAS Macros for Computing the Mediated Effect in the Pretest-Posttest Control Group Design David P. MacKinnon source 10
SGF 2017 Using PROC SEVERITY to Evaluate Quantile Approximation Techniques for Compound Distributions Helgard Raubenheimer,
Riaan de Jongh		 source 11
SESUG 2016 Introduction to Data Simulation Jason Brinkley source 8
SESUG 2016 Missing Data and Complex Sample Surveys Using SAS: The Impact of Listwise Deletion vs. Multiple Imputation on Point and Interval Estimates when Data are MCAR and MAR Anh P. Kellermann,
DeAnn Trevathan,
Jeffrey D. Kromrey		 source 12
SESUG 2016 Mixture Priors 101: Using SAS to Obtain Powerful Frequentist Inferences with Bayesian Methods Tyler Hicks,
Jeffrey D. Kromrey		 source 8
PHUSE EU 2016 EnForeSys - An Advanced Patient Enrolment Forecaster with Monte Carlo Simulations Ritika Yadav,
Swechhya Bista		 source
presentation source		 12
MWSUG 2016 Using SAS to Generate p-Values with Monte Carlo Simulation Brandy R. Sinco,
Edith Kieffer,
Michael S. Spencer,
Michael Woodford,
Gloria Palmisano,
Gretchen Piatt,
Michele Heisler		 14
WUSS 2016 MIghty PROC MI to the rescue! Cynthia Alvarez source 10
SGF 2016 Bayesian Inference for Gaussian Semiparametric Multilevel Models Jason Bentley,
Cathy Yuen Yi Lee		 source
zip		 19
SCSUG 2015 Using SAS Software to Determine the Mixed Rotterdam Demand Model Parameters and Test Statistical References Maryam Tabarestani,
Hector Zapata		 23
MWSUG 2015 Intuitive Demonstration of Statistics through Data Visualization of Pseudo-Randomly Generated Numbers in R and SAS Jack Sawilowsky 11
MWSUG 2015 Using SAS for the Longitudinal Analysis of Difference Scores Brandy R. Sinco,
Edith Kieffer,
Michael S. Spencer,
Gloria Palmisano,
Gretchen Piatt,
Michele Heisler		 14
SESUG 2015 Regulatory Stress Testing - A Manageable Process with SAS Wei Chen,
John Pechacek,
Erik Leaver,
Shannon Clark		 source 12
WUSS 2015 Sample Size Convergence of Denominator Degrees of Freedom Method Options in SAS PROC MIXED Timothy Banh source 13
PharmaSUG CN 2015 Clinical trials optimization: Monte Carlo Simulation modeling and SAS applications Ye Meng 5
SGF 2015 Regulatory Stress Testing - A Manageable Process with SAS Wei Chen,
John Pechacek,
Erik Leaver,
Shannon Clark		 source 12
SGF 2015 SAS Analytics plus Warren Buffett's Wisdom Beats Berkshire Hathaway! Huh? Bruce Bedford source 20
SGF 2015 Validation and Monitoring of PD Models for Low Default Portfolios Using PROC MCMC Machiel Kruger source 16
PHUSE EU 2014 Small Sample Bayesian Factor Analysis Dirk Heerweghm source
presentation source		 18
PharmaSUG CN 2014 Calculation of Confidence Intervals for Odds Ratio and Binomial Ratio Using StatXact PROCs Halimu Haridona 7
WUSS 2014 Fuzzy Bootstrap Matching Dale Harrington source 9
WUSS 2014 Using SAS to do it all Chelsea Lofland,
Rebecca Ottesen		 source 16
SGF 2014 A SAS Macro to Diagnose Influential Subjects in Longitudinal Studies Grant Schneider,
Randy Tobias		 source
zip		 18
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 Modeling Ordinal Responses for a Better Understanding of Drivers of Customer Satisfaction Jorge Alejandro,
Sharon Kim		 source 8
SGF 2014 Non-Empirical Modeling: Incorporating Expert Judgment as a Model Input [presentation] John Parker source 20
SGF 2014 Uncovering Trends in Research Using SAS Text Analytics with Examples from Nanotechnology and Aerospace Engineering Tom Sabo source 16
WUSS 2013 Simulation in SAS with Comparisons to R Chelsea Lofland,
Rebecca Ottesen		 source 25
SESUG 2013 Area under a Receiving Operating Characteristic (ROC) Curve: comparing parametric estimation, Monte Carlo simulation and numerical integration Receiver Operating Characteristic source 16
SESUG 2013 SAS Macros CORR_P and TANGO: Interval Estimation for the Difference Between Correlated Proportions in Dependent Samples Patricia Rodríguez de Gil,
Jeanine L. Romano,
Thanh Pham,
Diep T. Nguyen,
Jeffrey D. Kromrey,
Eun Sook Kim		 source 9
SESUG 2013 Using Predetermined Factor Structures to Simulate a Variety of Data Conditions Kevin Coughlin,
Fort Myers		 source 12
PharmaSUG 2013 Bayesian Analysis of Survival Data with SAS PHREG Procedure Ryan Brady source 8
SGF 2013 Exploring Time Series Data Properties in SAS Aude Pujula,
David Maradiaga,
Hector Zapata		 source 12
SGF 2013 Predicting Health Care Expenditures with the MCMC Procedure Gregory Watson source 12
NESUG 2012 Financial Impact Analysis of the New RUG-IV on Post-Acute Medicare Providers Using Monte Carlo Simulation John Gao,
Cheryl Caswell,
Barry Fogel		 13
NESUG 2012 Generalized Additive Models in Marketing Mix Modeling Revisited Patralekha Bhattacharya 9
SGF 2012 A SAS Macro for Computing Point Estimates and Confidence Intervals of Effect Sizes Associated with Mediation Analysis Merlande Petit-Bois,
Thanh V. Pham,
Eun Kyeng Baek,
Jeffrey D. Kromrey		 source 12
SGF 2012 Weighted Portmanteau Tests Revisited: Detecting Heteroscedasticity, Fitting Nonlinear and Multivariate Time Series Thomas J. Fisher source 12
SESUG 2011 CI_MEDIATE: A SAS Macro for Computing Point and Interval Estimates of Effect Sizes Associated with Mediation Analysis Thanh V. Pham,
Eun Kyeng Baek,
Merlande Petit-Bois,
Jeffrey D. Kromrey		 source 10
WUSS 2011 Assessing resource adequacy uncertainty in California electricity summer demand using the MCMC procedure in SAS Enterprise MinerTM Miguel Garcia-Cerrutti source 12
MWSUG 2011 The Full Monty Joe Roma,
Irvin Snider		 12
NESUG 2011 A Comparison of PDLREG and GAM Procedures in Measuring Dynamic Effects Patralekha Bhattacharya 11
NESUG 2011 Cash-Flow Modeling for Fixed Income Securities? A New Approach Using SAS Risk Management for Banking Solution Sunny Zhang 14
PharmaSUG 2011 Using SAS for Modeling and Simulation in Drug Development - A Review and Assessment of Some Available Tools Melvin S. Munsaka,
Michael Carniello		 source 10
SGF 2011 Cash Flow Modeling for Fixed-Income Securities: A New Approach Using the SAS Risk Management for Banking Solution Sunny Zhang,
Jeff Hasmann,
Thomas Kimner		 source 14
SGF 2011 Developing User-Defined Functions in SAS: A Summary and Comparison Songfeng Wang,
Jiajia Zhang		 source 9
SGF 2011 Impact of Regulations on Risk and Trading Analytics in Capital Markets Jeff Hong source 13
SGF 2011 Marketing Investment Optimization Framework for AmEx Travelers Cheques, Clustering Consumer Behavior Leveraging SAS/ETS to Uncover Investment Opportunities in an Environment Demanding Quick Decisions Landon Starr,
Pieter van Ispelen		 source 11
SGF 2011 MCPOWER: a Flexible Macro Suite for Generating Monte Carlo Power Estimates for Linear, Logistic, and Poisson Regression Models Ken Kleinman source 5
SGF 2011 PATH and LISMOD Languages in SAS PROC TCALIS for Multigroup Comparisons Fei Gu,
Wei Wu		 source 10
NESUG 2010 Using Generalized Additive Models in Marketing Mix Modeling Patralekha Bhattacharya 8
MWSUG 2010 Ad Hoc and Statistical Model Visualization Using JMP, SAS, and Excel Jon T. Weisz 10
MWSUG 2010 Monty Hall, Paul Erdös, and Monte Carlo Irvin Snider 7
MWSUG 2010 Power to Detect Therapist Effects in Randomized, Controlled Trials of Interventions in Clinical Psychology Douglas Thompson,
Fary Cachelin,
Ruth Striegel-Moore,
Terry Wilson		 16
SESUG 2010 Ad Hoc and Statistical Model Visualization Using JMP, SAS, and Excel Jon T. Weisz source 10
SGF 2010 Ad Hoc and Statistical Model Visualization Using JMP, SAS, and Excel Jon T. Weisz source 10
SGF 2010 Dancing the Sample-Size Limbo with Mixed Models: How Low Can You Go? Bethany A. Bell,
Grant B. Morgan,
Jason A. Schoeneberger,
Brandon L. Loudermilk,
Jeffrey D. Kromrey,
John M. Ferron		 source 11
SGF 2010 Data Simulation for Piloting Clinical Trials Display Programs Brandon L. Welch,
Rita Slater		 source 9
SGF 2010 Effective Visualization Techniques for Data Discovery and Analysis Charles Pirrello source 9
SGF 2010 Introduction to SAS Risk Management for Banking Austin Trippensee source 8
WUSS 2009 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell source 18
WUSS 2009 Using SAS for Nonparametric Statistics Gerry Hobbs source 7
PharmaSUG 2009 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell 18
SGF 2009 Analysis of Survival Data with Clustered Events Lida Gharibvand,
Lei Liu		 source 11
SGF 2009 Parsimony vs. Complexity: A Monte Carlo Investigation of Hierarchical and Cross-Classified Modeling Using SAS PROC MIXED Bethany A. Bell,
Corina M. Owens,
Jeffrey D. Kromrey,
John M. Ferron		 source 12
WUSS 2008 Comparing efficiency of cross validated partial least squares regression and ridge regression models in predicting spatial variability of total nitrogen concentration in Truckee River, Nevada. Anpalaki J. Ragavan source 13
SGF 2008 Operational Risk: Economic Capital (EC) Modeling with a Loss Distribution Approach Sabri G. Uner source 14
NESUG 2007 An Algorithm to Compute Exact Power of an Unordered RxC Contingency Table Vivek Pradhan,
Stian Lydersen		 8
WUSS 2007 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell source 18
PHUSE EU 2007 Bayesian Modelling with SPLUS and the S+flex Bayes library Andrew Jack,
Dawn Woodard		 source 11
PNWSUG 2007 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell 18
PharmaSUG 2007 A Review of Propensity Score Application in Healthcare Outcome and Epidemiology Guiping Yang 24
PharmaSUG 2007 Cochran-Armitage Trend Test Using SAS Hui Liu 11
PharmaSUG 2007 SAS Markov Chain Monte Carlo (MCMC) Simulation in Practice Shi-Tao Yeh,
Scott D Patterson		 20
SGF 2007 Customer Case Study: The Evolution of Financial Risk Analytics at EnCana Gord Anderson,
Mark Perrin		 source 15
SGF 2007 Don't Be Loopy: Re-Sampling and Simulation the SAS Way David L. Cassell source 18
SGF 2007 GLMM_SIM: A SAS Macro for Evaluating the Statistical Integrity of General Linear Mixed Models James T. Hess,
Melinda R. Hines,
Constance Kromrey,
Jeffrey D. Lee,
Reginald S. Luther,
Stephen L. Coraggio		 source 10
SESUG 2006 MLM_SIM: A SAS Macro for the Statistical Investigation of Mixed Models Jeffrey D. Kromrey source 15
SUGI 2006 %hlmdata and %hlmrmpower: Traditional Repeated Measures vs. HLM for Multilevel Longitudinal Data Analysis?Power and Type I Error Rate Comparison Hua Fang source 11
SUGI 2006 Empirical Power Estimation for Phase I Dose-Proportionality Studies Based on Power-Law Model Using Confidence Interval Criteria Jihao Zhou,
Jane Li,
Bruce Coate,
Melissa Chen		 source 11
SUGI 2006 Exploiting the Link Between the Wilcoxon-Mann-Whitney Test and a Simple Odds Statistic Ralph G. O'Brien,
John M. Castelloe		 source 12
SCSUG 2005 Basis Risk Quantification Using SAS Allen Bryson,
Kevin Kindall,
Xianqiao Chen		 18
NESUG 2005 Algorithm and Software for Yield Analysis Igor Mandel,
Victor Kamensky		 16
SUGI 2005 Empirical Power for Higher-Order Crossover Designs in Comparative Bioavailability Clinical Trials Jihao Zhou,
Bruce Coate		 source 10
SUGI 2005 Power Simulation for Categorical Data Using the RANTBL Function Joel A. Dubin,
Theodore R. Holford,
Peter H. Van Ness		 source 12
NESUG 2004 SAS and Grid Computing - Maximize Efficiency, Lower Total Cost of Ownership Cheryl Doninger 6
WUSS 2004 A System to Calculate Market Value-at-Risk using Oracle and Monte Carlo Simulation in SAS/IML George Rezek source 12
WUSS 2004 SAS and Grid Computing - Maximize Efficiency, Lower Total Cost of Ownership Cheryl Doninger source 6
MWSUG 2004 A System to Calculate Market Risk George Rezek 11
PNWSUG 2004 A System to Calculate Market Value-at-Risk using Oracle and Monte Carlo Simulation George Rezek 12
PharmaSUG 2004 Statistical Simulations for Sample Size Calculation with PROC IML Izabella Peszek 8
SUGI 2004 Empirical Power Computation Using SAS for Schuirmann's Two One-Sided Tests Procedure in Clinical Pharmacokinetic Drug-Drug Interaction Studies Jihao Zhou,
Bruce Coate,
Yulei He		 source 9
SUGI 2004 SAS and Grid Computing - Maximize Efficiency, Lower Total Cost of Ownership Cheryl Doninger source 7
SUGI 2003 A SAS/IML Program for Mapping QTL in Line Crosses Chenwu Xu,
Shizhong Xu		 source 6
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
SESUG 2002 Using SAS to Control Multistream Binomial Processes with a Chi-Squared Control Chart Peter S. Wludyka source 5
WUSS 2002 An Overview of Remedial Tools for the Violation of Parametric Test Assumptions in the SAS System Chong Ho Yu source 6
SEUGI 2002 Introduction to SAS Risk Management [presentation] Mika Hakuni source 24
SEUGI 2002 The problem of treasurey portfolio optimisation [presentation] Alexey Kulemin,
Vasily Konuzin,
Vasily Sorokoumov,
Victor Kulyshev		 source 52
PharmaSUG 2002 Fast Two-Sample Permutation Tests, Even When One Sample is Large, that Efficiently Maximize Power Under Crude Monte Carlo Sampling John D. Opdyke 10
SESUG 2001 P713 Using SAS To Control Multistream Binomial Processes Peter S. Wludyka,
Sheri Jacobs		 source 8
SCSUG 2001 Using SAS to Control MultiStream Binomial Processes Peter S. Wludyka,
Sheri Jacobs		 8
PharmaSUG 2001 Imputation Techniques Using SAS Software For Incomplete Data In Diabetes Clinical Trials Naum M. Khutoryansky,
Won-Chin Huang		 4
SESUG 2000 Using SAS to Perform Robust I-Sample Analysis of Means Type Randomization Tests for Variances Peter S. Wludyka source 6
NESUG 2000 How to Be a Bayesian in SAS: Model Selection Uncertainty in PROC LOGISTIC and PROC GENMOD Ernest S. Shtatland 9
SEUGI 2000 Risk Dimensions V3 Overview [presentation] Rik van de Weerthof source 28
SEUGI 2000 Risk Management System [presentation] Jaime Penaranda source 24
SCSUG 1999 Normalization of Peak Demand for an Electric Utility Using PROC Model J. Mark Harris,
Jeff Brown,
Mark Gilbert		 7
MWSUG 1999 A General Gibbs Sampling Algorithm for Analyzing Linear Models Using the SAS System Jayawant N. Mandrekar,
Daniel J. Sargent,
Paul J. Novotny,
Jeff A. Sloan		 6
SEUGI 1999 An introduction to risk management paper stream [presentation] Rik van de Weerthof source 33
SEUGI 1999 Quasi-Monte Carlo applications for financial risk management [presentation] Maurizio Mondello,
Maurizio Ferconi		 source 28
SUGI 1999 A General Gibbs Sampling Algorithm for Analyzing Linear Models Using the SAS System Daniel J. Sargent,
Jayawant N. Mandrekar,
Paul J. Novotny,
Jeff A. Sloan		 source
SUGI 1999 Integrating Market and Credit Risks with SAS Institute's Risk Dimensions Software Sam Harris source 8
SUGI 1999 Simulation and Estimation of Fractionally Integrated Time Series Jeffrey S. Butler source 6
SUGI 1999 Study Design with SAS: Estimating Power with Monte Carlo Methods Robert W. Graebner source 3
SUGI 1999 Using SAS Macros to Develop Confidence Intervals for the Weibull and Extreme Value Distributions Using Type II Censored Data Scott M. Jordan,
Muhammad Zaman		 source 6
MWSUG 1998 A Tour of UnifyPow: A SAS Module/Macro for Sample-Size Analysis Ralph G. O'Brien 11
SESUG 1998 Multiple Comparison Procedures with SAS Chung-Kuei Chang 9
SUGI 1998 A Tour of UnifyPow: A SAS Module/Macro for Sample-Size Analysis Ralph G. O'Brien source 10
SUGI 1998 Using SAS for Statistical Modeling: Monte Carlo Simulations Sean W. Mulvenon,
M. Austin		 source 6
PNWSUG 1997 Extreme Events Terry Pease 4
NESUG 1997 A Self-Organizing Median-Estimation Model Susan B. Garavaglia 9
SEUGI 1996 An object-oriented simulation and forecasting tool with the SAS System Wilfried Schollenberger source 6
SEUGI 1996 Risk management with the SAS System Christian J. Balica source 31
WUSS 1995 Heteroskedasticity in Regression Model - Detection and Remedial Measures Using SAS 6.10 George C. J. Fernandez source 10
MWSUG 1994 Alternative Approaches to Mixed Linear Models George W. Pasdirtz 6
SEUGI 1994 Cross Validation of Nearest-neighbour Discriminant Analysis Allan White source 15
SEUGI 1994 VALITYPO: une Macro pour valider statistiquement une typologie Christian Derquenne source 12
SUGI 1994 Nonlinear Regression Analysis and Nonlinear Simulation Models Donald Erdman,
Mark Little		 source 9
SUGI 1994 Small-Sample Inference in SAS Software: Using Exact Methods Cyrus R. Mehta source 9
SUGI 1994 Using SAS Software for Monte Carlo Simulation Vi-Cheng Wu,
James E. McLean		 source 3
NESUG 1993 Interfacing StatXact and SAS for Exact Tests Cyrus R. Mehta,
David C. Schlotzhauer		 9
MWSUG 1993 Generalized Least Squares Estimation in Heteroscedastic Linear Models George W. Pasdirtz 7
SEUGI 1993 Simulation Models for Business Planning and Economic Forecasting Liza Evans source 7
SUGI 1993 Bayesian Statistics in SAS Software William J. Raynor Jr. source 10
SUGI 1993 Model Estimation with Inequality Constraints: A Bayesian Approach Using SAS/IMLSoftware Charles B. Hallahan source 7
SUGI 1993 MONTE-A Risk Analysis Program Richard H. Fleming source 6
SUGI 1993 SIMSPRT: A SAS Code for Simulation of Sequential Probability Ratio Tests Kenny C. Gross,
Kristin K. Hoyer		 source 6
SUGI 1993 Simulation Models for Business Planning and Economic Forecasting Donald Erdman source 7
MWSUG 1992 A Monte Carlo Evaluation of the Rank Transformation for Cross-Over Trials George W. Pasdirtz 6
SEUGI 1992 Experiences from Using the SAS System in a Large Consumption Simulation Study Timo Alanko source 11
SUGI 1992 A Simulation Study of Estimators for Rates of Changes in Longitudinal Studies with Attrition Fong Wang,
Mary Lange		 source 6
SUGI 1992 Extending the SAS System's Statistical Capabilities with SAS/IML Software Charles B. Hallahan source 10
SUGI 1992 Statistical Simulations Using the CALIS Procedure David P. MacKinnon source 5
MWSUG 1991 Monte Carlo Analysis of Change Scores David J. Mangen 6
SUGI 1991 Power Analysis and Instructional Monte Carlo Simulations Richard W. Cole source 4
SUGI 1991 Using SAS/GRAPH Software to Create Trees that Model Developmental Biological Phenomena Perry Watts,
Samuel Litwin		 source 6
SUGI 1991 Using SAS/IML Software in a New Approach to Dealing with Multicollinearity Kevin D. Brunson,
Lawrence C. Marsh		 source 5
NESUG 1990 Exact Nonparametric Inference: Theory, Applciations and Software Cyrus R. Mehta 16
MWSUG 1990 Monte Carlo Analysis of Change Scores David J. Mangen 14
SUGI 1990 Nonsymmetric Bootstrap Confidence Intervals Using SAS/IML Software on a PC George W. Pasdirtz source 4
SUGI 1990 The Distribution of Cronbach's Coefficient Alpha from the CORR Procedure of SAS Software Andrew J. Cucchiara,
Susan J. Kenny,
J. Paul Costiloe		 source 6
SUGI 1990 Unbiased Estimators of Shrinkage for Chi-Square Based Measures of Association Richard F. Haase source 6
SUGI 1989 A Class of Highly Efficient Resistant and Robust Estimators for Simple Linear Regression for Use with the SAS System Evelyn A. Adler source 6
SUGI 1988 Exact and Monte Carlo Methods for Computing Non-Parametric Significance Tests Cyrus R. Mehta,
Nitin R. Patel,
Pralay Senchaudhuri		 source 6
SUGI 1987 The Analysis of Mapped Spatial Point Patterns E. Barry Moser source 5
SUGI 1986 Using the SAS System to Perform Power Analyses for Log-Linear Models Ralph G. O'Brien source 7
SEUGI 1985 New Features of the SAS/ETS and SAS/OR Software in Version 5 John C. Brocklebank source 37
SEUGI 1985 The Best Mode of Use for the Cubic Clustering Criterion Jean-Luc Molliere source 23
SUGI 1985 A Monte Carlo Comparison of Extended Mantel-Haenszel Procedures Daniel W. Griffin source 6
SUGI 1985 Changes and Enhancements to SAS/ETSSoftware David M. DeLong,
Leigh A. Ihnen,
Mark Little		 source 12
SUGI 1985 The SAS System as a Statistical Simulation Language Timothy J. Breen,
Robert M. Hamer		 source 8
SEUGI 1984 New Statistical Features in SAS Gerhard Held source 13
SUGI 1984 A Queueing Theory Model for Optimization Using SAS Software Chitra B. Prabhakar source 4
SUGI 1984 Changes and Enhancements to SAS/ETS Procedures David M. DeLong,
Leigh A. Ihnen,
Mark Little		 source 6
SUGI 1984 The Use of SAS Software in New Product Introductions Joyce D. Geier source 5
SUGI 1983 Effect Size and Power in Two Factor Nonorthogonal ANOVA: A Monte Carlo Investigation Margaret J. Martin,
William A. Powers		 source
SUGI 1983 SAS Methods for Standard Error of Estimate in Maximum Likelihood Factor Analysis .. Susanna F.M. Clarkson,
Douglas B. Clarkson		 source 5
SUGI 1983 SAS: A Decision Support System for Management Raymond A. Gibley,
Heidi J. Schoen		 source 6
SUGI 1982 Approaches to Estimating Covariance Matrices with Partial Multinomial Data Mark W. Riggs,
W.B. Smith		 source 10
SUGI 1982 The Application of Risk Analysis to Marketing Information Support Systems William R. Gjertsen source
SUGI 1981 A SAS Macro for Generating Patient/Subject Random Allocation Schedules James A. Bolognese source 3
SUGI 1979 Estimating Passenger Demands from Truncated Samples Franklin S. Young source 5

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