Tag: Simulation

Rick Wicklin 0
The new book on my desk

In my constant effort to keep pace with Chris Hemedinger, I am pleased to announce the availability of my new book, Simulating Data with SAS. Chris started a tradition for SAS Press authors to post a photo of themselves with their new book. Thanks to everyone who helped with the

Rick Wicklin 0
Efficient acceptance-rejection simulation

A few days ago on the SAS/IML Support Community, there was an interesting discussion about how to simulate data from a truncated Poisson distribution. The SAS/IML user wanted to generate values from a Poisson distribution, but discard any zeros that are generated. This kind of simulation is known as an

Rick Wicklin 0
Constructing common covariance structures

I recently encountered a SUGI30 paper by Chuck Kincaid entitled "Guidelines for Selecting the Covariance Structure in Mixed Model Analysis." I think Kincaid does a good job of describing some common covariance structures that are used in mixed models. One of the many uses for SAS/IML is as a language

Rick Wicklin 0
That distribution is quite PERT!

There are a lot of useful probability distributions that are not featured in standard statistical textbooks. Some of them have distinctive names. In the past year I have had contact with SAS customers who use the Tweedie distribution, the slash distribution, and the PERT distribution. Often these distributions are used

Rick Wicklin 0
Generate uniform data in a simplex

It is easy to simulate data that is uniformly distributed in the unit cube for any dimension. However, it is less obvious how to generate data in the unit simplex. The simplex is the set of points (x1,x2,...,xd) such that Σi xi = 1 and 0 ≤ xi ≤ 1

Rick Wicklin 0
The curious case of random eigenvalues

I've been a fan of statistical simulation and other kinds of computer experimentation for many years. For me, simulation is a good way to understand how the world of statistics works, and to formulate and test conjectures. Last week, while investigating the efficiency of the power method for finding dominant

Mike Gilliland 0
Forecasting and analytics at Disney World

The April 2012 issue of ORMS Today contains a piece on "How analytics enhance the guest experience at Walt Disney World," by Pete Buczkowski and Hai Chu. While many of us are used to forecasting just one or two things (such as unit sales or revenue), Pete and Hai illustrate

Rick Wicklin 0
Generating a random orthogonal matrix

Because I am writing a new book about simulating data in SAS, I have been doing a lot of reading and research about how to simulate various quantities. Random integers? Check! Random univariate samples? Check! Random multivariate samples? Check! Recently I've been researching how to generate random matrices. I've blogged

Advanced Analytics
Rick Wicklin 0
Use the Cholesky transformation to correlate and uncorrelate variables

A variance-covariance matrix expresses linear relationships between variables. Given the covariances between variables, did you know that you can write down an invertible linear transformation that "uncorrelates" the variables? Conversely, you can transform a set of uncorrelated variables into variables with given covariances. The transformation that works this magic is

Rick Wicklin 0
Random number seeds: Only the first seed matters!

The other day I encountered the following SAS DATA step for generating three normally distributed variables. Study it, and see if you can discover what is unnecessary (and misleading!) about this program: data points; drop i; do i=1 to 10; x=rannor(34343); y=rannor(12345); z=rannor(54321); output; end; run; The program creates the

Rick Wicklin 0
How to lie with a simulation

In my article on Buffon's needle experiment, I showed a graph that converges fairly nicely and regularly to the value π, which is the value that the simulation is trying to estimate. This graph is, indeed, a typical graph, as you can verify by running the simulation yourself. However, notice

Rick Wicklin 0
Simulation of Buffon's needle in SAS

Buffon's needle experiment for estimating π is a classical example of using an experiment (or a simulation) to estimate a probability. This example is presented in many books on statistical simulation and is famous enough that Brian Ripley in his book Stochastic Simulation states that the problem is "well known

Rick Wicklin 0
The great Christmas gift exchange revisited

One aspect of blogging that I enjoy is getting feedback from readers. Usually I get statistical or programming questions, but every so often I receive a comment from someone who stumbled across a blog post by way of an internet search. This morning I received the following delightful comment on

Rick Wicklin 0
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

Rick Wicklin 0
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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