(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Mon: Recently in the sister blog
Tues: How should statisticians and economists think about recreational gambling?
Wed: Lack of free lunch again rears ugly head
Thurs: Swimsuit special: “A pure Bayesian or pure non-Bayesian is not forever doomed to use out-of-date methods, but at any given time the purist will be missing some of the most effective current techniques.”
Fri: I definitely wouldn’t frame it as “To determine if the time series has a change-point or not.” The time series, whatever it is, has a change point at every time. The question might be, “Is a change point necessary to model these data?” That’s a question I could get behind.
Sat: Actually, I’d just do full Bayes
Sun: My quick answer is that I would analyze all 10 outcomes using a multilevel model.
And, just for fun, here are the posts scheduled for the following week:
Mon: Thinking about this beautiful text sentiment visualizer yields a surprising insight about statistical graphics
Tues: The Psychological Science stereotype paradox
Wed: In defense of endless arguments
Thurs: Multilevel regression
Fri: Data-dependent priors as an approximation to hierarchical priors
Sat: Objects of the class “Pauline Kael”
Sun: What is a Republican?
We’re actually booked through June but this is all I’ll give ya right now.
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