| NESUG |
2004 |
SAS and Neural Networking: Spatial / Functional Maps of Biological Systems |
|
|
7 |
| NESUG |
2004 |
SAS COWs: Parallel Clusters and Financial Market Simulation |
|
|
6 |
| NESUG |
2004 |
Serving the Kitchen Sink: SAS Portfolio Analysis |
|
|
12 |
| SUGI |
2004 |
Financial COWs: Using SAS to Manage Parallel Clusters for Simulating Financial Markets |
|
source |
7 |
| SUGI |
2004 |
Risk Management : Using SAS to Model Portfolio Drawdown, Recovery, and Value at Risk |
|
source |
10 |
| NESUG |
2002 |
Real Neural Networking: Using SAS To Build And Deliver Behavior-Genetic-Nervous Spatial And Functional Maps Of Biological Systems |
|
|
5 |
| NESUG |
2002 |
Temporal Optimization: Convergence of Time Series and Linear Modeling for Selecting Portfolio Strategies |
|
|
3 |
| NESUG |
2002 |
Visual Optimization: Using SAS And Java To Explore High Density Data In Equity Portfolios |
Ezra Benun |
|
5 |
| PharmaSUG |
2002 |
SAS Cluster Disk Management: Using SAS to Manage Virtual Disks on a Cluster of File Servers |
Ezra Benun |
|
6 |
| PharmaSUG |
2002 |
SAS Cluster Gene Matching: Using SAS To Manage Computer Clusters And To Perform Protein Matching |
|
|
6 |
| PharmaSUG |
2002 |
Simulating Attrition and Visual Models of Type I and Type II Errors |
|
|
10 |
| SUGI |
2002 |
SAS Cluster Disk Management: Using SAS to Manage Virtual Disks on a Cluster of File Servers |
Ezra C. Benun |
source |
5 |
| NESUG |
2001 |
Lost in Space: Recovering unmeasured dimensions in spatial studies |
|
|
8 |
| NESUG |
2001 |
PROTEOMIC COWS: Using SAS to Manage and Run Parallel Clusters for Gene Searching |
|
|
6 |
| NESUG |
2001 |
Simulating Attrition and Visual Models of Type I and Type II Errors |
|
|
9 |
| SUGI |
2001 |
Growing Genetic Cows with SAS: Using Low Cost Parallel Processing With Linux - MS/NT Networks to Search Genomes |
|
source |
6 |
| NESUG |
2000 |
Seeking, Mapping, and Fuzzy Merging Data Structures in a Networked Environment |
|
|
8 |
| NESUG |
1999 |
Visual Analysis on the WEB: Animating High Density Multidimensional Data |
|
|
9 |