Months ago, I asked a question to the community: how should I organize my R research projects? After writing that post, doing some reading, then putting a plan in practice, I now have my own answer.
Now here is a blog post that has been sitting on the shelf far longer than it should have. Over a year ago I wrote an article about problems I was having when estimating the parameters of a GARCH(1,1) model in R. I documented the behavior of parameter estimates (with a focus on ) and perceived pathological behavior when those estimates are computed using fGarch. I called for help from the R community, including sending out the blog post over the R Finance mailing list.
Over the past few weeks I’ve published articles about my new package, MCHT, starting with an introduction, a further technical discussion, demonstrating maximized Monte Carlo (MMC) hypothesis testing, bootstrap hypothesis testing, and last week I showed how to handle multi-sample and multivariate data. This is the final article where I explain the capabilities of the package. I show how MCHT can handle time series data.
I’ve spent the past few weeks writing about MCHT, my new package for Monte Carlo and bootstrap hypothesis testing. After discussing how to use MCHT safely, I discussed how to use it for maximized Monte Carlo (MMC) testing, then bootstrap testing. One may think I’ve said all I want to say about the package, but in truth, I’ve only barely passed the halfway point!
Today is the first day of the new academic year at the University of Utah. This semester I am teaching MATH 3070: Applied Statistics I, the fourth time I’ve taught this course.
I’m pleased to announce my fourth and final video course. The course has already been out for a couple months by now, but that doesn’t mean it’s too late for me to write about it!