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.
A previous article discusses how to generate a random correlation matrix. On average, in a random correlation matrix, half of the off-diagonal entries are negative and half are positive. For any realization, the proportion of negative correlations might be greater than (or less than) half. This is in contrast to
In a previous article, I implemented an algorithm due to Niels Waller (TAS, 2020) that uses the method of alternating projections (MAP) to generate random correlation matrices that have a specified set of eigenvalues. The algorithm is iterative, and the MAP method is not guaranteed to converge, although Waller claims
I've been working on a project that uses quasi-Monte Carlo (QMC) techniques to estimate probabilities for multivariate normal (MVN) distributions on finite or infinite rectangular regions. The goal is to enable SAS users to compute these probabilities accurately and efficiently. My implementation is based on a numerical technique called the
