It is a mild summer evening in July at Lake Neusiedl here in Austria. The participants of the traditional YES Cup Regatta are sitting with beer and barbecue chops on the terrace of our clubhouse. The mood is relaxed, and everyone wants to tell their story after two eventful races.
The SAS/IML language is used for many kinds of computations, but three important numerical tasks are integration, optimization, and root finding. Recently a SAS customer asked for help with a problem that involved all three tasks. The customer had an objective function that was defined in terms of an integral.
Es ist ein lauer Sommerabend im Juli am Neusiedler See. Die Teilnehmer der traditionellen YES-Cup Regatta sitzen bei Bier und Grillkotelette auf der Terrasse unseres Clubhauses. Die Stimmung ist ausgelassen, jeder hat nach zwei ereignisreichen Wettfahrten seine Geschichte zu erzählen. Eine Unterhaltung am Ende unseres Tisches zieht meine Aufmerksamkeit auf
This blog is a continuation of an earlier blog entitled “To grid or not to grid?” In that blog, one of the reasons to say “yes to SAS Grid” is to see if you can gain some performance improvements from modifying your existing SAS processes by converting them to a
I want to use SAS’ recent announcement of our Cost and Profitability Management solution as an opportunity to highlight an often overlooked but valuable application of activity-based costing: business process reengineering. But first, just a brief description of Cost and Profitability Management’s new breakthrough capability: In-memory model calculation. SAS’ decision
My last blog post showed how to simulate data for a logistic regression model with two continuous variables. To keep the discussion simple, I simulated a single sample with N observations. However, to obtain the sampling distribution of statistics, you need to generate many samples from the same logistic model.
In my book Simulating Data with SAS, I show how to use the SAS DATA step to simulate data from a logistic regression model. Recently there have been discussions on the SAS/IML Support Community about simulating logistic data by using the SAS/IML language. This article describes how to efficiently simulate
A colleague asked me an interesting question: I have a journal article that includes sample quantiles for a variable. Given a new data value, I want to approximate its quantile. I also want to simulate data from the distribution of the published data. Is that possible? This situation is common.
I've pointed out in the past that in the SAS/IML language matrices are passed to modules "by reference." This means that large matrices are not copied in and out of modules but are updated "in place." As a result, the SAS/IML language can be very efficient when it computes with
In 1990, the North Carolina General Assembly created the Sentencing and Policy Advisory Commission to evaluate sentencing laws and policies and recommend any modifications necessary to achieve policy goals. As part of the mandate, the General Assembly required the Sentencing Commission to develop a correctional population simulation model. The model
I love working with SAS Technical Support because I get to see real problems that SAS customers face as they use SAS/IML software. The other day I advised a customer how to improve the efficiency of a computation that involved multiplying large matrices. In this article I describe an important
Analytics gives us not just the ability but the imperative to separate our planning activities into two distinct segments – detailed planning that leads to budgets in support of execution, and high-level, analytic-enabled business/scenario planning. My critique of Control Towers in this blog last time led me not only to
The volume is being turned up on the Control Tower approach to running a business; I have recently been introduced to logistics control towers, supply chain control towers and operations control towers just for starters. I’m sure there must be at least a half dozen more out there – pick
Once again I'll be at SAS Global Forum this year. The 2014 location is Washington, D. C., so I am looking forward to greeting many friends in the government and consulting sectors. I always enjoy talking with SAS customers about statistics, simulations, matrix computations, and the SAS/IML product, so here's
I had the opportunity to moderate a roundtable discussion on risk management at the International Institute for Analytics’ (IIA) winter symposium in Orlando earlier this month. I set the stage for the session with a brief overview of my favorite risk approach, “Competing on Value”, by Mack Hannan and Peter
Randomly choosing a subset of elements is a fundamental operation in statistics and probability. Simple random sampling with replacement is used in bootstrap methods (where the technique is called resampling), permutation tests and simulation. Last week I showed how to use the SAMPLE function in SAS/IML software to sample with
I began 2014 by compiling a list of 13 popular articles from my blog in 2013. Although this "People's Choice" list contains many articles that I am proud of, it did not include all of my favorites, so I decided to compile an "Editor's Choice" list. The blog posts on
Vector languages such as SAS/IML, MATLAB, and R are powerful because they enable you to use high-level matrix operations (matrix multiplication, dot products, etc) rather than loops that perform scalar operations. In general, vectorized programs are more efficient (and therefore run faster) than programs that contain loops. For an example
In 2013 I published 110 blog posts. Some of these articles were more popular than others, often because they were linked to from a SAS newsletter such as the SAS Statistics and Operations Research News. In no particular order, here are some of my most popular posts from 2013, organized
This is the time of year when we like to make predictions about the upcoming year. Although I am optimistic about the potential of predictive analytics in the era of big data, I am also realistic about the nature of predictability regardless of how much data is used. For example, in
Each year my siblings choose names for a Christmas gift exchange. It is not unusual for a sibling to pick her own name, whereupon the name is replaced into the hat and a new name is drawn. In fact, that "glitch" in the drawing process was a motivation for me
“The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.” ~ Warren G. Bennis The promise of digital manufacturing is drawing closer to
Don Wedding played a baseball simulation game called Sports Illustrated/Avalon Hill Superstar Baseball back when he was in grade school in Toledo, Ohio. The game involved rolling specialized dice, and then referring to cards representing the performance of the greatest baseball players of all time. The problem was Wedding knew
Do your SAS programs read extra-large volumes of data? Do they run multiple DATA steps and procedures one after the other for hours at a time? Two papers from MWSUG 2013 show how you can speed up those long-running SAS jobs. Although their approaches and environments differed, both authors made
Im fortschreitende Modernisierungsprozess befindet sich die öffentliche Hand im Spannungsfeld zwischen knappen Kassen und demografischem Wandel einerseits und Forderungen nach einer wirkungsorientierten und bürgernahen Auftragserfüllung andererseits. Die zunehmende Komplexität der fachlichen Zusammenhänge innerhalb einzelner Politikfelder und der Abhängigkeiten zwischen verschiedenen Bereichen bindet zusätzliche Ressourcen. Mehr und mehr fordert die Politik
This article describes how to implement the truncated normal distribution in SAS. Although the implementation in this article uses the SAS/IML language, you can also implement the ideas and formulas by using the DATA step and PROC FCMP. For reference, I recommend the Wikipedia article on the truncated normal distribution.
There are many techniques for generating random variates from a specified probability distribution such as the normal, exponential, or gamma distribution. However, one technique stands out because of its generality and simplicity: the inverse CDF sampling technique. If you know the cumulative distribution function (CDF) of a probability distribution, then
Are you still using the old RANUNI, RANNOR, RANBIN, and other "RANXXX" functions to generate random numbers in SAS? If so, here are six reasons why you should switch from these older (1970s) algorithms to the newer (late 1990s) Mersenne-Twister algorithm, which is implemented in the RAND function. The newer
Being an author can be a tough gig. Lots of writing, reviews, edits, cover approvals…the list goes on and on. But often the good times outweigh the tough times. And we’re so lucky that many of our authors have written more than one book for SAS Press. One such author
As I wrote in my previous post, a SAS customer noticed that he was getting some duplicate values when he used the RAND function to generate a large number of random uniform values on the interval [0,1]. He wanted to know if this result indicates a bug in the RAND
