This post will grow to cover questions about data reduction methods, also known as unsupervised learning methods. These are intended primarily for two purposes:
collapsing correlated variables into an overall score so that one does not have to disentangle correlated effects, which is a difficult statistical task reducing the effective number of variables to use in a regression or other predictive model, so that fewer parameters need to be estimated The latter example is the “too many variables too few subjects” problem.
As a biostatistics teacher I’ve spent a lot of time thinking about inverting the classroom and adding multimedia content. My first thought was to create YouTube videos corresponding to sections in my lecture notes.
Professor of Biostatistics
Vanderbilt University School of Medicine
Professor of Psychiatry and, by courtesy, of Medicine (Cardiovascular Medicine) and of Biomedical Data Science
Stanford University School of Medicine
Misinterpretation of P-values and Main Study Results Dichotomania Problems With Change Scores Improper Subgrouping Serial Data and Response Trajectories Cluster Analysis As Doug Altman famously wrote in his Scandal of Poor Medical Research in BMJ in 1994, the quality of how statistical principles and analysis methods are applied in medical research is quite poor.
While being engaged in biomedical research for a few decades and watching reproducibility of research as a whole, I’ve developed my own ranking of reliability/quality/usefulness of research across several subject matter areas.
I discussed the many advantages or probability estimation over classification. Here I discuss a particular problem related to classification, namely the harm done by using improper accuracy scoring rules. Accuracy scores are used to drive feature selection, parameter estimation, and for measuring predictive performance on models derived using any optimization algorithm.
The difference between Bayesian and frequentist inference in a nutshell:
With Bayes you start with a prior distribution for θ and given your data make an inference about the θ-driven process generating your data (whatever that process happened to be), to quantify evidence for every possible value of θ.