Estimating Prevalence using Imperfect Classifiers
How to correct prevalence estimates using PPI
How to correct prevalence estimates using PPI
I did a three-day adventure racing stage race
Calibration is an important idea in statistical prediction. However, it’s not the only thing.
I find a strange plot of p-values from a hypothesis test and investigate.
SMBC had a interesting comic recently on correlation chains. It suggests the new “Funtime Activity” of creating correlation chains. As a statistician, I naturally wanted to evaluate the methodology. The basic idea is that you start with a given variable X1 (“Amount of Sex” in the comic) and start by linking to a positively correlated variable X2 (“Happiness”). Then X2 is positively correlated to X3 (“Income”) so you expand the chain to X1 → X2 → X3. Eventually you end up at variable Xn (“Likelihood that you are, in fact, J.K. Rowling”) that’s you conclude is positively correlated the variable X1. ...
How do you sample from a stream of unknown length?