Over the last five years, gamdist has formed the backbone of my research agenda. While it is very much a work in progress, this paper summarizes everything I have learned about regression. I think it is most useful as a collection of references! Still to come: details on regularization and the alternating direction method of multipliers.

We present a method of orbit determination robust against non-normal measurement errors. We approach the non-convex optimization problem by repeatedly linearizing the dynamics about the current estimate of the orbital parameters, then minimizing a convex cost function involving a robust penalty on the measurement residuals and a trust region penalty.

We discuss a beer recommendation engine that predicts whether a user has had a given beer as well as the rating the user will assign that beer based on the beers the user has had and the assigned ratings. k-means clustering is used to group similar users for both prediction problems. This framework may be valuable to bars or breweries trying to learn the preferences of their demographic, to consumers wondering what beer to order next, or to beer judges trying to objectively assess quality despite subjective preferences.