Cette semaine, on finit la régression de Poisson (temporairement) avant de présenter la théorie des GLM. Les transparents sont en ligne. On en aura besoin pour aller plus loin sur les modèles avec surdispersion, pour modéliser la fréquence de sin...

Cette semaine, on finit la régression de Poisson (temporairement) avant de présenter la théorie des GLM. Les transparents sont en ligne. On en aura besoin pour aller plus loin sur les modèles avec surdispersion, pour modéliser la fréquence de sin...

Let us continue our discussion on smoothing techniques in regression. Assume that . where is some unkown function, but assumed to be sufficently smooth. For instance, assume that is continuous, that exists, and is continuous, that exists and is also continuous, etc. If is smooth enough, Taylor’s expansion can be used. Hence, for which can also be writen as for some ‘s. The first part is simply a polynomial. The second…

In a standard linear model, we assume that . Alternatives can be considered, when the linear assumption is too strong. Polynomial regression A natural extension might be to assume some polynomial function, Again, in the standard linear model approach (with a conditional normal distribution using the GLM terminology), parameters can be obtained using least squares, where a regression of on is considered. Even if this polynomial model is not the…

Francisco tells me that they have uploaded my talk (which I gave last week in ULPGC). I haven't seen it all, but the bit I did see is not too bad, I thought... Check it out!

Andrew Gelman recently commented on the difficulties of measuring or interpreting just about anything, and gave an example about sexual harassment in the Marine Corps. I wanted to relay a story. There is no general conclusion to be drawn that I can see...

Editor’s Note: This guest post was written by Elizabeth C. Matsui, an Associate Professor in the Division of Pediatric Allergy and Immunology at the Johns Hopkins School of Medicine. I’ve been collaborating with Roger for several years now and we … Continue reading →

Daniel Sgroi and Andrew Oswald write: Many governments wish to assess the quality of their universities. A prominent example is the UK’s new Research Excellence Framework (REF) 2014. In the REF, peer-review panels will be provided with information on publications and citations. This paper suggests a way in which panels could choose the weights to […]The post A Bayesian approach for peer-review panels? and a speculation about Bruno Frey appeared…

*in this post, by "black-box" I'm referring to software whose methods are undisclosed and un-audible rather than black-box math models There’s a certain expectation that the analyses that inform not only business and stock trading, but public health and social welfare decisions, are carefully thought out and performed with painstaking attention to detail. However, the »more

As my group has grown over the past few years and I have more people writing software, I have started to progressively freak out more and more about how to make sure that the software is sustainable as students graduate … Continue reading →

This one is fun because I have a double conflict of interest: I’ve been paid (at different times) both by Google and by Microsoft. Here’s the story: Microsoft, September 2012: An independent research company, Answers Research based in San Diego, CA, conducted a study using a representative online sample of nearly 1000 people, ages 18 […]The post Bing is preferred to Google by people who aren’t like me appeared first…

In the first chapter of my first book, Numbers Rule Your World (link), I explored the concept of variability using a pair of examples, one of which was Disney's FastPass virtual reservation system. Truly grasping the ins and outs of variability is one of the most important objectives for a budding statistician (or data scientist). In the discussion, I highlighted the work of Len Testa, whose website, TouringPlans.com, provides custom,…

Get data that fit before you fit data. Why verify? Garbage in, garbage out. How to verify The example data used here is daily (adjusted) prices of stocks. By some magic that I’m yet to fathom, market data can be wondrously wrong even without the benefit of the possibility of transcription errors. It doesn’t seem … Continue reading →

What is the best way to share SAS/IML functions with your colleagues? Give them the source code? Create a function library that they can use? This article describes three techniques that make your SAS/IML functions accessible to others. As background, remember that you can define new functions and subroutines in [...]

My office computer recently got a really nice upgrade and now I have 8 cores on my desktop to play with. I also at the same time received some code for a Gibbs sampler written in R from my adviser. I wanted to try a metropolis-coupled markov chain monte carlo, , algorithm on it to […] The post Parallel Tempering in R with Rmpi appeared first on Lindons Log.

I consider presentation and storytelling the next step in visualization, after most of the focus has been on exploration and analysis so far. An upcoming version of Tableau will include a feature called Story Points, which supports presentation directly in the visualization tool. A Story A Tableau Story is a new type of sheet, like […]

A fascinating read about applying decision theory to mathematical proofs. They talk about Type I and Type II errors and everything. Statistical concepts explained through dance. Even for a pretty culture-deficient dude like me this is cool. Lots of good … Continue reading →