A Toeplitz matrix is a banded matrix. You can construct it by specifying the parameters that are constant along each diagonal, including sub- and super-diagonals. For a square N x N matrix, there is one main diagonal, N-1 sub-diagonals, and N-1 super-diagonals, for a total of 2N-1 parameters. In statistics and applied
Assigning observations into clusters can be challenging. One challenge is deciding how many clusters are in the data. Another is identifying which observations are potentially misclassified because they are on the boundary between two different clusters. Ralph Abbey's 2019 paper ("How to Evaluate Different Clustering Results") is a good way
In SAS, you can approximate the exponential of a matrix by using the EXPMATRIX function in SAS IML software. This article discusses the exponential of a matrix: what it is, how to compute it, why it is useful, and why you should think of it as a linear map that
You can use a Markov transition matrix to model the transition of an entity between a set of discrete states. A transition matrix is also called a stochastic matrix. A previous article describes how to use transition matrices for stochastic modeling. You can estimate a Markov transition matrix by using
Recently, I needed to write a program that can provide a solution to a regression-type problem, even when the data are degenerate. Mathematically, the problem is an overdetermined linear system of equations X*b = y, where X is an n x p design matrix and y is an n x 1 vector. For most
Did you know that there is a mathematical formula that simplifies finding the derivative of a determinant? You can compute the derivative of a determinant of an n x n matrix by using the sum of n other determinants. The n determinants are for matrices that are equal to the original matrix
Some matrices are so special that they have names. The identity matrix is the most famous, but many are named after a researcher who studied them such as the Hadamard, Hilbert, Sylvester, Toeplitz, and Vandermonde matrices. This article is about the Pascal matrix, which is formed by using elements from
You can use the Cholesky decomposition of a covariance matrix to simulate data from a correlated multivariate normal distribution. This method is encapsulated in the RANDNORMAL function in SAS/IML software, but you can also perform the computations manually by calling the ROOT function to get the Cholesky root and then
In a previous article, I discussed various ways to solve a least-square linear regression model. I discussed the SWEEP operator (used by many SAS regression routines), the LU-based methods (SOLVE and INV in SAS/IML), and the QR decomposition (CALL QR in SAS/IML). Each method computes the estimates for the regression
In computational statistics, there are often several ways to solve the same problem. For example, there are many ways to solve for the least-squares solution of a linear regression model. A SAS programmer recently mentioned that some open-source software uses the QR algorithm to solve least-squares regression problems and asked
