The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs
When you use the bootstrap method in statistics, the most common resampling method is called case resampling. For data that has N observations, each bootstrap sample is created by sampling with replacement from the N observations (or "cases") in the data. However, if the data set includes categorical variables, it
A recent article describes the main features of simulation by using the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE was created to oversample from a set of rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) provides a way
The Synthetic Minority Over-sampling Technique (SMOTE) was created to address class-imbalance problems in machine learning algorithms. The idea is to oversample from the rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) is essentially a way to simulate
SAS programmers love to brag that the SAS will still run a program they wrote twenty or forty years. This is both a blessing and a curse. It's a blessing because it frees the statistical programmer from needing to revisit and rewrite code that was written long ago. It's a
Isaac Newton had many amazing scientific and mathematical accomplishments. His law of universal gravitation and his creation of calculus are at the top of the list! But in the field of numerical analysis, "Newton's Method" was a groundbreaking advancement for solving for a root of a nonlinear smooth function. The
Newton's method was in the news this week. Not the well-known linear method for finding roots, but a more complicated method for finding minima, sometimes called the method of successive parabolic approximations. Newton's parabolic method was recently improved by modern researchers who extended the method to use higher-dimensional polynomials. The