In case you missed the obvious, I'm Jacob Simmering. By day, I am a graduate student at the University of Iowa and by night, well, I am pretty much the same thing. I am interested in statistical computing and applied statistics, especially in the domains of healthcare and health economics.
Various projects doing fun and interesting stuff (by my standards) can be found at my GitHub page. Commentary on those fun projects, or whatever else sounds interesting at the moment, will be found on my blog.
My most recent blog posts
Parallel Simulation of Heckman Selection Model
One of the major problems in observational research is estimating the true treatment effect. This is not hard when the selection and outcome processes are uncorrelated and all relevant variables are observed and properly controlled for. However, when the selection and outcome are correlated and it is not possible to remove this correlation on the basis of the observables, biased estimation results. The Heckman selection model affords one way of dealing with and minimizing this introduced bias. A parallel R based simulation of a Heckman style estimator compared to least squares and propensity scores highlights the potential utility of this framework.
The Problem with Propensity Scores
Propensity scores are increasingly in vogue as a way to adjust for differences between populations in estimating treatment effects. Some view propensity scores as an almost mythical way of dealing with confounding. However, they are limited to adjustment for the observables, just like standard regression. So it raises the question "how do propensity scores compare as an estimator relative to linear regression?" The answer is short --- "not well."
Frequentist German Tank Problem
When you go to war, it can be useful to know how many tanks the other side has. However, they often refuse to tell you. Worse even, they will often vastly inflate production numbers. They are at war, after all. If only there was a way to convert that pesky sequential serial number to an estimate of the total number of tanks...
Stop using bivariate correlations for variable selection
You need to come up with a regression model for some response. You have tons of predictor variables that you might want to consider. How do you decide what variables to consider in your model? If you started with bivariate correlations of the response and each predictor, you may be in for some trouble.
Bayesian Search Models
Whether you have lost a sub, hydrogen bomb or just your keys, Bayesian search theory can help find it!