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July 2015  |  subscribe  |  unsubscribe
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It’s All about 14.1

It's rare that a newsletter is perfectly timed right after a new software release, but I am putting this material together the day after the 14.1 releases of the SAS® analytical products shipped with SAS 9.4M3. It's also rare that my newsletter-writing day coincides with the Major League Baseball All-Star Game in the United States, but there you have it. (For those who care, the American League won and has the home-field advantage during the next World Series.)

While I seriously doubt that sabermetrics had any role in the strategy for the All-Star Game, no doubt the data scientists running the show at many professional sports teams will find something to help their "game" in the new releases of SAS analytical products. From SAS/STAT® 14.1 and SAS/OR® 14.1 to SAS® Forecast Server and the new SAS® Factory Miner, there truly is a wealth of new features and capabilities to help you keep your eye on the analytical ball.

We prepared a 14.1 Resource Guide that provides highlights and links to more detailed product overviews, What's New chapters, and release documentation. In addition, a number of the features in this newsletter provide a glimpse of some of the most important new work in the various analytical products and a look at the new SAS Factory Miner.

If you are headed to the Joint Statistical Meetings, August 8–13 in Seattle, you can stop by the SAS area in the exhibition halls and see the 14.1 release in person. We're excited about this third release of additional content for SAS 9.4, and we hope you will be, too. Note that the Talks and Tutorials section describes the courses we are presenting at JSM in case you are interested.

Many of our econometricians are headed to the 2015 World Congress of the Econometric Society, August 17–21 in Montreal, so feel free to ask them questions about SAS/ETS® 14.1 if you notice a "SAS" on their badge.

Here's to a great rest of the summer.

Senior R&D Director, Statistical Applications

Featured Papers
SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data »

The latest release of SAS/STAT software brings you powerful techniques that will make a difference in your work, whether your data are massive, missing, or somewhere in the middle. New imputation software for survey data adds to an expansive array of methods in SAS/STAT for handling missing data, as does the production version of the GEE procedure, which provides the weighted generalized estimating equation approach for longitudinal studies with dropouts. An improved quadrature method in the GLIMMIX procedure means you can fit models not previously possible. The HPSPLIT procedure provides a rich set of methods for statistical modeling with classification and regression trees, including cross validation and graphical displays. The HPGENSELECT procedure adds support for LASSO model selection for generalized linear models. And new software implements generalized additive models by using an approach that handles large data easily. Other updates include key functionality for Bayesian analysis and pharmaceutical applications.

Using the PHREG Procedure to Analyze Competing-Risks Data »

Competing risks arise in studies in which individuals are subject to a number of potential failure events and the occurrence of one event might impede the occurrence of other events. For example, after a bone marrow transplant, a patient might experience a relapse or might die while in remission. You can use one of the standard methods of survival analysis, such as the log-rank test or Cox regression, to analyze competing-risks data, whereas other methods, such as the product-limit estimator, might yield biased results. An increasingly common practice of assessing the probability of a failure in competing-risks analysis is to estimate the cumulative incidence function, which is the probability subdistribution function of failure from a specific cause. This paper discusses two commonly used regression approaches for evaluating the relationship of the covariates to the cause-specific failure in competing-risks data. One approach models the cause-specific hazard, and the other models the cumulative incidence. The paper shows how to use the PHREG procedure in SAS/STAT software to fit these models.

Introducing the HPGENSELECT Procedure: Model Selection for Generalized Linear Models and More »

Generalized linear models are highly useful statistical tools in a broad array of business applications and scientific fields. How can you select a good model when numerous models that have different regression effects are possible? The HPGENSELECT procedure, which was introduced in SAS/STAT 12.3, provides forward, backward, and stepwise model selection for generalized linear models. In SAS/STAT 14.1, the HPGENSELECT procedure also provides the LASSO method for model selection. You can specify common distributions in the family of generalized linear models, such as the Poisson, binomial, and multinomial distributions. You can also specify the Tweedie distribution, which is important in ratemaking by the insurance industry and in scientific applications. You can run PROC HPGENSELECT in single-machine mode on the server where SAS/STAT is installed. With a separate license for SAS® High-Performance Statistics, you can also run the procedure in distributed mode on a cluster of machines that distribute the data and the computations. This paper shows you how to use PROC HPGENSELECT both for model selection and for fitting a single model. The paper also explains the differences between the HPGENSELECT procedure and the GENMOD procedure.

Using the OPTMODEL Procedure in SAS/OR to Solve Complex Problems »

Mathematical optimization is a powerful paradigm for modeling and solving business problems that involve interrelated decisions about resource allocation, pricing, routing, scheduling, and similar issues. The OPTMODEL procedure in SAS/OR software provides unified access to a wide range of optimization solvers and supports both standard and customized optimization algorithms. This paper illustrates PROC OPTMODEL's power and versatility in building and solving optimization models and describes the significant improvements that result from PROC OPTMODEL's many new features. Highlights include the recently added support for the network solver, the constraint programming solver, and the COFOR statement, which allows parallel execution of independent solver calls. Best practices for solving complex problems that require access to more than one solver are also demonstrated.

Ten Tips for Simulating Data with SAS »

Data simulation is a fundamental tool for statistical programmers. SAS software provides many techniques for simulating data from a variety of statistical models. However, not all techniques are equally efficient. An efficient simulation can run in seconds, whereas an inefficient simulation might require days to run. This paper presents 10 techniques that enable you to write efficient simulations in SAS. Examples include how to simulate data from a complex distribution and how to use simulated data to approximate the sampling distribution of a statistic.

Explaining the Past and Modeling the Future: An Overview of Econometrics Tools in SAS/ETS »

The importance of econometrics in the analytics toolkit is growing every day. Econometric modeling helps uncover structural relationships in observational data. This paper highlights the many recent changes to the SAS/ETS software portfolio that increase your power to explain the past and predict the future. Examples show how you can use Bayesian regression tools for price elasticity modeling, use state space models to gain insight from inconsistent time series, use panel data methods to help control for unobserved confounding effects, and much more.

Count Series Forecasting »

Many organizations need to forecast large numbers of time series that are discretely valued. These series, called count series, fall approximately between continuously valued time series, for which there are many forecasting techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are few forecasting techniques (Croston's method and others). This paper proposes a technique for large-scale automatic count series forecasting and uses SAS Forecast Server and SAS/ETS software to demonstrate this technique.

SAS® University Edition Going Out on the Town at JSM »

You know I love SAS University Edition, our free download of Base SAS®, SAS/STAT, and SAS/IML® software, so I'll take the opportunity to mention that SAS will hold a reception in honor of it and other free academic software programs during the Joint Statistical Meetings in Seattle. If you are a professor or student who is using or interested in using SAS software like SAS University Edition to provide training in statistics, you might like to attend the Sunday-night reception at Pike Place Market and learn more or talk with current users. Please request your invitation today!

Demo of New SAS Factory Miner »

Besides enhancing many products with the 14.1 release, SAS introduced SAS Factory Miner. This brand-new software provides an automated framework for building, comparing, and retraining hundreds, even thousands, of models quickly, easily, and automatically. You work through a web-based, drag-and-drop interface to build models. The software tests multiple models simultaneously by using statistical and machine learning algorithms, and it identifies the best-performing model for each segment based on predefined performance statistics. If you are not satisfied with the automated approach, you can manually fine-tune each model. If your shop has the need for automated model building, SAS Factory Miner might just do the trick. Take a look.

Web Example: Handling Spatial Data in Spherical Coordinates »

The SPP procedure and other spatial analysis procedures in SAS/STAT are designed to handle projected coordinate systems. If your data are collected in a spherical coordinate system—for example, longitude and latitude—then you should convert it to a projected system before applying PROC SPP. This example walks you through a sequence of steps that demonstrate how to handle data that have spherical coordinates in order to analyze them by using PROC SPP.

The DO Loop »

You might need to turn off ODS output for different reasons: for example, in simulation and bootstrap studies, you might analyze 10,000 or more samples or resamples. There are three ways to suppress ODS output in a SAS procedure: the NOPRINT option, the ODS EXCLUDE statement, and the ODS CLOSE statement. This blog post compares these approaches in terms of their efficiency, ease of use, and portability.

One of the fundamental principles of computer programming is to break a task into smaller subtasks and to modularize the program by encapsulating each subtask into its own function. This blog post describes how best to do this by writing a SAS/IML module.

Talks and Tutorials
At JSM 2015 »

August 8–13, 2015

Seattle, WA

Introducing the SAS BCHOICE Procedure for Bayesian Choice Models (Computer Technology Workshop)

Analyzing Item Responses with the IRT Procedure: An Introduction with Applications (CTW)

Practical Finite Mixture Model with SAS (CTW)

Practical Bayesian Computation (full day)

Quantile Regression in Practice (half day)

Introduction to Structural Equation Modeling and Its Applications (half day)

At WUSS 2015 »

September 9–11, 2015

San Diego, CA

Modeling Longitudinal Categorical Response Data


Tech Support Points Out
Testing for Model Nonconvergence or Fitting Error in a Macro »

Most modeling procedures that use an iterative fitting algorithm (such as maximum likelihood or generalized estimating equations) produce, by default, a table indicating the convergence status of the model. The name of this table, for use in ODS statements, is ConvergenceStatus. The GENMOD, GLIMMIX, LOGISTIC, MIXED, and GAM procedures are just a few of the procedures that create a ConvergenceStatus table. Find out how to use this table to obtain more information about your model fitting process.

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