I recently received a copy of Numbersense by Kaiser Fung for review. Fung is the author of a blog I have a lot of respect for : Junk Charts. The current post at the top of Junk Charts is about...

Devrup Ghatak writes: I am a student of economics and recently read your review of Mostly Harmless Econometrics. In the review you mention that the book contains no time series. Given that your book on data analysis (Data Analysis using Regression) does not contain any time series material either, I wonder if you happen to […]The post Somebody’s looking for a book on time series analysis in the style of…

When explaining about the Excel automation work we do at Sharp Statistics often the initial response is ‘so you write VBA macros’. In fact we don’t use Visual Basic for Applications (VBA) macros to build our solutions but instead use ...

Antonio Rinaldi writes: Here in Italy an “hype” topic is the “staffetta tra generazioni”, handover between generations: since unemployment rate in young people is very high, someone in the government is thinking to encourage older people to anticipate their retirement to make more jobs available for youngs. I am not an economist and I don’t […]The post Learning about correlations using cross-sectional and over-time comparisons between and within countries appeared…

Following up on our discussion from the other day, Angelika van der Linde sends along this paper from 2012 (link to journal here). And Aki pulls out this great quote from Geisser and Eddy (1979): This discussion makes clear that in the nested case this method, as Akaike’s, is not consistent; i.e., even if $M_k$ […]The post More on AIC, WAIC, etc appeared first on Statistical Modeling, Causal Inference, and…

I wouldn’t go that far, but I’ll send along this article by Ahti-Veikko Pietarinen that was sent to me by Lee Sechrest. Those of you who like this sort of thing might like this sort of thing. I neither endorse nor anti-endorse. Or, I should say, I am in sympathy with the author’s general attitude […]The post Blaming scientific fraud on the Kuhnians appeared first on Statistical Modeling, Causal Inference,…

Today, I have the honor of interviewing Avinash Kaushik, author of the bible known as Web Analytics 2.0, and a digital marketing evangelist at Google. He also has a must-read blog called Occam's Razor. Occam's Razor is a principle championed by statisticians that can be summarized as "as simple as possible but not too simple". It is a principle and therefore it also draws controversy from some quarters. Kaushik's blog…

With permission from my colleague Aris Spanos, I reblog his (8/18/12): “Egon Pearson’s Neglected Contributions to Statistics“. It illuminates a different area of E.S.P’s work than my posts here and here. Egon Pearson (11 August 1895 – 12 June 1980), is widely known today for his contribution in recasting of Fisher’s significance testing into the Neyman-Pearson (1933) […]

I recently came across the The 7Twelve Portfolio strategy. I like the catchy name and the strategy report, “An Introduction to 7Twelve.” Following is some additional info about the The 7Twelve Portfolio strategy that I found useful: On Israelsen’s 7Twelve Portfolio The 7/12 Allocation Today I want to show how to back-test the The 7Twelve […]

The post “What do we need to Model?” showed what our goal in modeling errors should be. This one shows how it’s achieved. Assigning a distribution to the fixed parameters is like finding a prior ; it’s successful whenever the &#...

Andreas Graefe writes (see here here here): The usual procedure for developing linear models to predict any kind of target variable is to identify a subset of most important predictors and to estimate weights that provide the best possible solution for a given sample. The resulting “optimally” weighted linear composite is then used when predicting […]The post The robust beauty of improper linear models in decision making appeared first on…

My course on non-life insurance (ACT2040) will start in a few weeks. I will use R to illustrate predictive modeling. A nice introduction for those who do not know R can be found online.

Avinash Kaushik's masterful post on the "mutli-channel attribution problem" in Web analytics is required reading for anyone seeking an understanding of what Big Data is really about. Kaushik's posts are marathons; I provide here a little background, plus some highlights from his post to save you some time. But you absolutely should read the whole thing! I will start from the elementary. Big Data is big because the Internet was…

The Akaike Information Critera (AIC) is a widely used measure of a statistical model. It basically quantifies 1) the goodness of fit, and 2) the simplicity/parsimony, of the model into a single statistic. When comparing two models, the one with the lower AIC is generally “better”. Now, let us apply this powerful tool in comparing […]

Our good colleagues Brian Caffo, Martin Lindquist, and Ciprian Crainiceanu have written a nice editorial for the HuffPo on the need for statisticians in neuroimaging.