learn more about statistics

I’m reading The Cartoon Guide to Statistics, which has got to go down as the most difficult-to-read comic book, ever.

Alas, I will not be able to attend the very cool class this upcoming quarter as I am just too damn busy to sit through it. Hopefully I will move somewhere else where stats classes are available to audit. I want to take a class on dependencies in data someday.

This most recent class was good, in that I plan on studying more on the advanced theory of the linear model and its extensions.

Well the class I sat in on this quarter has been pretty good. Not sure how much I will remember in 6 months, but it did give me some good solid foundations for further studies of Generalized Linear Models. I think I need to read a good book on mathematical statistics sometime. And re-read my big fat book on linear models.

Neural networks seem to be involved.

Huge list of ones and zeros describing an individual unit. About 100 individual units being observed/measured, perhaps 100,000 ones and zeros per unit. Need a soft modeling approach to use these 100,000 ones and zeros and 100 individual units to predict a set of measured (i.e. real) numbers (also from the 100 individuals).

Now that is a problem.

Well, I guess I now understand some of the basics of PLS (partial least-squares regression) but I am not sure if there is a point in learning all the details. I had a theory about how it related to other methods, but now it seems that the work is done or wrong. :-(

Need to look for something else or see something I am not seeing yet.

I suspect that some relationship exists among all three of ICA, PCA, and PLS. I think it allows me to use one to place bounds on the others. If the result is unknown, I want to mathematically establish it. If it is known, I want to publish it within the context of the field in which I work.

If that sounds like gibberish, then just replace it with “I have found a statistical project to work on.” :-)

I read an article on maximum likelihood versus least-squares today, now on to some papers about AIC/BIC and model selection!

...what I need to do this week is invent more statistics. Something about computing the distribution for some distances between partially-constrained vectors in R⁵ so that I can assess the goodness of estimate of a particular procedure. Any ideas, email me!

Discrete statistical analysis is what I need to focus on for the near term. Fortunately, it dovetails with my other projects. Excellent.