I love doing Math Reviews, for the random stuff I get to read. Here is a paper I probably would not have found:
MR3208124 Rom\’an, Jorge Carlos, Hobert, James P.\ and Presnell, Brett, On reparametrization and the Gibbs sampler, Statist. Probab. Lett. 91 (2014), 110–116.
The Gibbs sampler may be out of fashion with the Bayesian computation crowd these days, but the reparameterizations are still mysterious. I tried the PyMC3 NUTS sampler on the first example and it too has rather different mixing times: https://gist.github.com/aflaxman/be253895efd0e2962472
Filed under MCMC, Mysteries
This is my new obsession, does anyone know what I should know about self-reported occupation data in NHIS surveys?
I’m sure reading a lot lately. That is good. This week, I’m filling in for the PBF journal club, too, and today we’ll be discussing Ciesielski et al’s paper Cadmium Exposure and Neurodevelopmental Outcomes in U.S. Children, which uses 6 years of NHANES data to weigh the evidence that low levels of cadmium cause learning disabilities in children.
All the data is available on the CDC’s website, so I thought I’d take a look at it. Here is an interesting little plot that popped out: prevalence of parent-reported learning disabilities in 6-15 year olds as a function of income-to-poverty-line ratio.
Would you have expected that?
I read the The Girl With the Dragon Tattoo series recently, which was extremely engrossing. The first book has a bit of a health metrics theme, with each section prefaced with a shocking statistic about violence against women in Sweden. The second book has a bit of a math theme, with each section prefaced by a correct, if inane algebraic equation.
Also in the second book, the tattooed girl spends some time reading a strangely titled math book, Dimensions in Mathematics, and I liked the story enough to google the book, since it was presented with author and publisher. It turned out that this just revealed more mystery.
I’ve been watching really fun 10 minute talks lately on youtuble. They are put together by the Royal Society for the encouragement of Arts, Manufacturers, and Commerce (weird name, huh? It seems they prefer “RSA” for short. But I’m still enough of a computer scientist to think that acronym is taken.)
Here is one that crossed my inbox yesterday, a talk by Dan Pink about what motivates us:
I went to a talk a few weeks ago by Richard Wilkinson and Kate Pickett, global health researchers who have written a book called The Spirit Level. They were quick to explain that, while the name makes perfect sense in British English, it has been a source of continuing confusion in American English. What is a “spirit level”? It’s a building tool, a type of ruler with little bubbles in it to show when it is parallel to the ground. Maybe it’s called a carpenter level in the states, or just a level when the context is clear.
I would have called it “Inequality vs Stuff”, or at least that’s my description of the talk: a vast array of scatterplots showing the relationship between income inequality and different measurements of population health. Here is one that is typical for their case:
When they told the story, they started with a composite health index scattered against inequality, since that has much less noise, and then use the noisy plots like this one as supporting evidence when they show that the relationship holds for everything.
The slide that stuck with me the most is one that diverged from their story a little:
Not population health this time, but still interesting. Something to share with your entrepreneur friends.
These plots seem like enough fun that I made my own, based on a question from the question and answer portion of the talk. I’ve forgotten who, but someone in the audience asked “How is inequality related to total fertility rate?” and the answer from Wilkinson and Pickett was along the lines of “We never thought to check, how do you think it might be related?”
Since I had the data lying around from my attempts to learn about model selection last summer, I made myself the plot. Turns out there is not much of an association.The only example of a non-association the speakers mentioned was a surprise to them: suicide rates are not correlated with income inequality.