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I’m writing this the day after the great solar eclipse in the United States. At SAS headquarters in Cary, North Carolina, we got 92% coverage, and as you can imagine, this resulted in a steady stream of staff heading outside and gazing upwards with their protective glasses. Numerous fellow workers headed to the nearby locations in the mountains and surrounding states that were in the path of totality, and we are sure to hear stories (including traffic woes) and see amazing pictures in the weeks to come.

Nothing like the galaxy to inspire the rest of your summer!

We’re shipping up to Boston on September 29 for the Boston Area SAS Users Group quarterly meeting, which is focused on analytics. Speakers from the SAS Advanced Analytics R&D division will speak on machine learning and Bayesian methods, among other topics. Check BASUG’s website in the coming weeks for more details.

The regional SAS users group meetings begin with WUSS, September 20–22 in Long Beach, California, and offer other opportunities to hear SAS staff present tutorials and talks.

Here’s to a good fall.

Maura Stokes

Senior R&D Director, Statistical Applications


Technical Papers


Automatic Singular Spectrum Analysis and Forecasting

The singular spectrum analysis (SSA) method of time series analysis applies nonparametric techniques to decompose time series into principal components. SSA is particularly valuable for long time series, in which patterns (such as trends and cycles) are difficult to visualize and analyze. An important step in SSA is determining the spectral groupings; this step can be automated by analyzing the w-correlations (weighted correlations) of the spectral components. This paper provides an introduction to singular spectrum analysis and demonstrates how to use SAS/ETS® software to perform it. To illustrate, monthly data on temperatures in the United States for about the last 100 years are analyzed to discover significant patterns.


Detecting and Adjusting Structural Breaks in Time Series and Panel Data Using the SSM Procedure

Detection and adjustment of structural breaks are an important step in modeling time series and panel data. In some cases, such as studying the impact of a new policy or an advertising campaign, structural break analysis might even be the main goal of a data analysis project. In other cases, the adjustment of structural breaks is a necessary step to achieve other analysis objectives, such as obtaining accurate forecasts and effective seasonal adjustment. Structural breaks can occur in a variety of ways during the course of a time series. For example, a series can have an abrupt change in its trend, its seasonal pattern, or its response to a regressor. The SSM procedure in SAS/ETS software provides a comprehensive set of tools for modeling different types of sequential data, including univariate and multivariate time series data and panel data. These tools include options for easy detection and adjustment of a wide variety of structural breaks.

This paper shows how you can use the SSM procedure to detect and adjust structural breaks in many different modeling scenarios. Several real-world data sets are used in the examples. The paper also includes a brief review of the structural break detection facilities of other SAS/ETS procedures, such as the ARIMA, AUTOREG, and UCM procedures.


More Than Matrices: SAS/IML® Software Supports New Data Structures

The SAS/IML language excels in handling matrices and performing matrix computations. A new feature in SAS/IML 14.2 is support for nonmatrix data structures such as tables and lists. In a matrix, all elements are of the same type: numeric or character. Furthermore, all rows have the same length. In contrast, SAS/IML 14.2 enables you to create a structure that contains many objects of different types and sizes. For example, you can create an array of matrices in which each matrix has a different dimension. You can create a table, which is an in-memory version of a data set. You can create a list that contains matrices, tables, and other lists.

This paper describes the new data structures and shows how you can use them to emulate other structures such as stacks, associative arrays, and trees. It also presents examples of how you can use collections of objects as data structures in statistical algorithms.


Telling the Story of Your Process with Graphical Enhancements of Control Charts

Have you ever used a control chart to assess the variation in a process? Did you wonder how you could modify the chart to tell a more complete story about the process? This paper explains how you can use the SHEWHART procedure in SAS/QC® software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore within-subgroup variation with box-and-whisker plots, and add information that improves the interpretability of the chart. The paper begins by reviewing the basics of control charts and then illustrates the enhancements with examples drawn from real-world quality improvement efforts.


Technical Highlights

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The DO Loop

Often your analysis produces prediction regions for classification problems, and it’s nice to visualize them. Distinguished Research Statistician Developer Rick Wicklin describes three methods of doing this. Rick also discusses how to choose a seed for generating random numbers in SAS®. Also, he shows how to perform a robust principal component analysis by using SAS/IML.


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Graphically Speaking 

In recent posts, Distinguished Research Statistician Developer Warren Kuhfeld discusses vector plots, employing basic ODS graphics, and equated axes and the aspect ratio




Tech Support Points Out

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Regularization, Regression Penalties, LASSO, Ridging, and Elastic Net

Regularization methods can be applied in order to shrink model parameter estimates in situations of instability. As discussed by Agresti (2013), one such situation occurs when there are a large number of covariates, of which only a small subset are strongly associated with the response, and the sample size is not large. In this case, the maximum likelihood estimates can be inflated. Parameter estimates can also be inflated by collinearity and by separation, in the case of logistic regression, among other reasons. Learn about regularization, regression penalties, and other ways to shrink model parameter estimates. 


Talks and Tutorials


Western Users of SAS Software (WUSS)  
September 20–22, 2017
Long Beach, CA

MidWest SAS Users Group (MWSUG) 
October 8–10, 2017
St. Louis, MO

Getting Started with Multilevel Modeling  
Mike Patetta
Power and Sample Size Computations
John Castelloe

Southeast SAS Users Group (SESUG) 
November 5–7, 2017
Research Triangle Park, NC

South Central SAS Users Group (SCSUG) 
October 15–17, 2017
Addison, TX

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