An analysis may require the ability to generate correlated random samples. For example, imagine we have monthly returns for three financial indicators over a 20 year period. We are interested in modeling these returns using parametric distributi...

The Discrete Fourier Transform (DFT) turns a data vector into a sum of sine/cosine components. The DFT is a Fourier series on data instead of analytic functions. Why do we perform the DFT? Because the features typically of interest aren’t always...

I recently became interested in GeoHashing, and wanted to develop an understanding of the algorithm with the goal of implementing it myself. I was surprised to find it to be quite simple and intuitive. In what follows, I’ll demonstrate how to ge...

An analysis may require the ability to generate correlated random samples. For example, imagine we have monthly returns for three financial indicators over a 20 year period. We are interested in modeling these returns using parametric distributio...

An analysis may require the ability to generate correlated random samples. For example, imagine we have monthly returns for three financial indicators over a 20 year period. We are interested in modeling these returns using parametric distributi...

Within the GLM framework, model coefficients are estimated using iterative reweighted least squares (IRLS), sometimes referred to as Fisher Scoring. This works well, but becomes inefficient as the size of the dataset increases: IRLS relies on th...

Bessel’s correction is the use of instead of in the sample variance formula where is the number of observations in a sample. This method corrects the bias in the estimation of the population variance. Recall that bias is defined as: where r...

The Discrete Fourier Transform (DFT) turns a data vector into a sum of sine/cosine components. The DFT is a Fourier series on data instead of analytic functions. Why do we perform the DFT? Because the features typically of interest aren’t always...