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Rick Wicklin
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Distinguished Researcher in Computational Statistics

Rick Wicklin, PhD, is a distinguished researcher in computational statistics at SAS and is a principal developer of SAS/IML software. His areas of expertise include computational statistics, simulation, statistical graphics, and modern methods in statistical data analysis. Rick is author of the books Statistical Programming with SAS/IML Software and Simulating Data with SAS.

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Video: Calling R from the SAS/IML Language

In SAS/IML 9.22 and beyond, you can call the R statistical programming language from within a SAS/IML program. The syntax is similar to the syntax for calling SAS from SAS/IML: You use a SUBMIT statement, but add the R option: SUBMIT / R. All statements in the program between the

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The "power" of finite mixture models

When I learn a new statistical technique, one of first things I do is to understand the limitations of the technique. This blog post shares some thoughts on modeling finite mixture models with the FMM procedure. What is a reasonable task for FMM? When are you asking too much? I

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Four essential functions for statistical programmers

Normal, Poisson, exponential—these and other "named" distributions are used daily by statisticians for modeling and analysis. There are four operations that are used often when you work with statistical distributions. In SAS software, the operations are available by using the following four functions, which are essential for every statistical programmer

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Does SYMPUT work in IML?

I received the following email: Dear Dr. Wicklin, Why doesn't SYMPUT work in IML? In the DATA step, I can say CALL SYMPUT("MyMacro", 5) but this doesn't work in IML! Frustrated Dear Frustrated, The SYMPUT subroutine does work in SAS/IML software! However, the second argument to SYMPUT must be a

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Optimizing? Two hints for specifying derivatives

I previously wrote about using SAS/IML for nonlinear optimization, and demonstrated optimization by maximizing a likelihood function. Many well-known optimization algorithms require derivative information during the optimization, including the conjugate gradient method (implemented in the NLPCG subroutine) and the Newton-Raphson method (implemented in the NLPNRA method). You should specify analytic

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Maximum likelihood estimation in SAS/IML

A popular use of SAS/IML software is to optimize functions of several variables. One statistical application of optimization is estimating parameters that optimize the maximum likelihood function. This post gives a simple example for maximum likelihood estimation (MLE): fitting a parametric density estimate to data. Which density curve fits the

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SAS/IML tip sheets

To celebrate the first anniversary of Statistical Programming with SAS/IML Software, you can now download the SAS/IML tip sheets (also called "cheat sheets") that I created for the book. At conferences, SAS Press displays these tip sheets next to my book. They have been very popular. Download these SAS/IML cheat

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