There has been a lot of discussion of peer review on this blog and elsewhere. One thing I realized is that no one ever formally taught me the point of peer review or how to write a review. Like a … Continue reading →

There has been a lot of discussion of peer review on this blog and elsewhere. One thing I realized is that no one ever formally taught me the point of peer review or how to write a review. Like a … Continue reading →

Our book is nearly out..! The Springer webpage is ready, we have sent the proofs back, amazon is missing has now included the above picture, things are moving towards the publication date, supposed to be November 30. Just in time for Christmas! And not too early given that we packed off in early February… Filed under: […]

Rob “Lasso” Tibshirani writes: We all read a lot of papers and often have useful things to say about them, but there is no systematic way to do this lots of journals have commenting systems, but they’re clunky, and, most importantly, they’re scattered across thousands of sites. Journals don’t encourage critical comments from readers, […]The post PubMed Commons: A system for commenting on articles in PubMed appeared first on…

A challenge for statistical programmers is getting data into the right form for analysis. For graphing or analyzing data, sometimes the "wide format" (each subject is represented by one row and many variables) is required, but other times the "long format" (observations for each subject span multiple rows) is more [...]

Last week in the non-life insurance course, we’ve seen the theory of the Generalized Linear Models, emphasizing the two important components the link function (which is actually the key component in predictive modeling) the distribution, or the variance function Just to illustrate, consider my favorite dataset lin.mod = lm(dist~speed,data=cars) A linear model means here where the residuals are assumed to be centered, independent, and with identical variance. If we visualize that linear…

There was a flurry of activity on social media yesterday surrounding a blog post by Lior Pachter. He was speaking about the GTEX project - a large NIH funded project that has the goal of understanding expression variation within and … Continue reading →

PubMed, the main database of life sciences and biomedical literature, is now allowing comments and upvotes. Here is more information and the twitter handle is @PubMedCommons.

For a good overview of what OCR is, check out this overview I found myself cutting the spines off books, again. This time it was because I couldn’t find an e-book copy of ‘Animal Liberation’ anywhere on the net, and I’ve amassed quite a few physical copies--mostly from garage sales--that I could afford to experiment »more

Nurit Baytch posted a document, A Critique of Ron Unz’s Article “The Myth of American Meritocracy”, that is relevant to an ongoing discussion we had on this blog. Baytch’s article begins: In “The Myth of American Meritocracy,” Ron Unz, the publisher of The American Conservative, claimed that Harvard discriminates against non-Jewish white and Asian students […]The post Ivy Jew update appeared first on Statistical Modeling, Causal Inference, and Social Science.

If you know any statistics, you know "sampling". It's the idea of measuring some subset of the population. Using the Law of Large Numbers, you are able to learn from the sample and generalize to the population. Your Stats professor never told you what "unsampling" is. You're not going to find this word in a statistics textbook either. What does it mean? The "un" implies that you can recover the…

What I’ve learned from updating the blogroll. New entries The easy option is to go to The Whole Street which aggregates lots of quant finance blogs. Somehow Bookstaber missed out being on the blogroll before — definitely an oversight. Timely Portfolio was another that I was surprised wasn’t already there. The R Trader talks about … Continue reading →

Attention conservation notice: Late notice of a very technical presentation about theoretical statistics in a city you don't live in. Today's speaker needs no introduction for those interested in modern, high-dimensional statistics (but will get an ...

Lecture 14: Why simulate? Generating random variables as first step. The built-in R commands: rnorm, runif, etc.; sample. Some uses of sampling: permutation tests; bootstrap standard errors and confidence intervals. Transforming uniformly-distribu...

Lecture 15: Combing multiple dependent random variables in a simulation; ordering the simulation to do the easy parts first. Markov chains as a particular example of doing the easy parts first. The Markov property. How to write a Markov chain simul...

The chapter (Chap. 3) on Bayesian updating or learning (a most appropriate term) for discrete data is well-done in Machine Learning, a probabilistic perspective if a bit stretched (which is easy with 1000 pages left!). I like the remark (Section 3.5.3) about the log-sum-exp trick. While lengthy, the chapter (Chap. 4) on Gaussian models has […]