Author

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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Optimizing a function of an integral

Last week I showed how to find parameters that maximize the integral of a certain probability density function (PDF). Because the function was a PDF, I could evaluate the integral by calling the CDF function in SAS. (Recall that the cumulative distribution function (CDF) is the integral of a PDF.)

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Optimizing a function that evaluates an integral

SAS programmers use the SAS/IML language for many different tasks. One important task is computing an integral. Another is optimizing functions, such as maximizing a likelihood function to find parameters that best fit a set of data. Last week I saw an interesting problem that combines these two important tasks.

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Sample without replacement in SAS

Last week I showed three ways to sample with replacement in SAS. You can use the SAMPLE function in SAS/IML 12.1 to sample from a finite set or you can use the DATA step or PROC SURVEYSELECT to extract a random sample from a SAS data set. Sampling without replacement

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Sample with replacement in SAS

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

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Say hello to SAS Analytics 13.1

Late last month, while many of us were sipping eggnog and decking halls with boughs of holly, SAS released the 13.1 version of its analytical products. Readers of Maura Stokes' newsletter, SAS Statistics and Operations Research News (Nov 2013), have already been alerted to new features in products such as

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How to vectorize time series computations

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

Rick Wicklin 0
A Christmas tree matrix

O Christmas tree, O Christmas tree, Last year a fractal made thee! O Christmas tree, O Christmas tree, A heat map can display thee! O tree of green, adorned with lights! A trunk of brown, the rest is white. O Christmas tree, O Christmas tree, A heat map can display

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Visualize a matrix in SAS by using a discrete heat map

A heat map is a graphical representation of a matrix that uses colors to represent values in the matrix cells. Heat maps often reveal the structure of a matrix. There are three common applications of visualizing matrices with heat maps: Visualizing a correlation or covariance matrix reveals relationships between variables.

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