Most statistical models used in experimental psychology are designed to estimate the mean of a response variable given some set of predictors. This is all well and good when errors are largely symmetric and our predictors are expected to primarily … Read moreComments closed
It’s difficult to analyze trajectory data, since the “object” of analysis (the trajectory) can’t easily be summarized by a single value without throwing away large amounts of information, unless you already know what you’re looking for (e.g. if you’re looking … Read moreComments closed
I’m swimming in reaction time data at the moment. My usual approach to analyzing reaction times in cognitive psychology is through some sort of normal random effects model of the log or inverse RT. This is really only good for … Read moreComments closed
Introductory statistics courses (particularly when they’re taught outside of the statistics department) often gloss over the details of the central limit theorem, describing it only as something that let’s you do t-tests without worrying about normality. I recently came across … Read moreComments closed
Echo-state networks (ESN’s) are a class of neural network designed to work with temporal data. I normally don’t like neural networks, since they rarely actually tell you anything interpretable about your data (and they pretty much just do regression anyway), … Read moreComments closed
I’ve been experimenting with techniques for robust regression, and I thought that it would be a fun excercise to implement a robust variant of the simple linear regression model based on the t-distribution.
The term “outlier” is used very … Read moreComments closed
I recently collaborated on a meta-analysis investigating the effects of blast-related (i.e. *BOOM*) mild traumatic brain injury (mTBI) on cognitive performance (Karr, et al. 2014). Each of the eight included studies used control and mTBI groups, and reported means and … Read moreComments closed