Blog Archives

GTC 2016

March 29, 2016
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GTC 2016

I will be an invited speaker at GTC 2016, a large conference on GPU computation. The main topic will be usage of GPU in conjunction with R, and I will also speak on my Software Alchemy method, especially in relation to GPU computing.. GTC asked me to notify my “network” about the event, and this … Continue reading...

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Even Businessweek Is Talking about P-Values

March 28, 2016
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Even Businessweek Is Talking about P-Values

The March 28 issue of Bloomberg Businessweek has a rather good summary of the problems of p-values, even recommending the use of confidence intervals and — wonder of wonders — “ at the evidence as a whole.” What, statistics can’t make our decisions for us?  :-) It does make some vague and sometimes puzzling statements, … Continue reading...

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P-values: the Continuing Saga

March 10, 2016
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P-values: the Continuing Saga

I highly recommend the blog post by Yoav Benjamini and Tal Galili in defense of (carefully used) p-values. I disagree with much of it, but the exposition is very clear, and there is a nice guide to relevant R tools, including for simultaneous inference, a field in which Yoav is one of the most prominent, … Continue reading...

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Further Comments on the ASA Manifesto

March 9, 2016
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Further Comments on the ASA Manifesto

On Tuesday I commented here on the ASA (in their words) “Position on p-values:  context, process, and purpose.” A number of readers replied, some of them positive, some mistakenly thinking I don’t think statistical inferences are needed, and some claiming I overinterpreted the ASA’s statement. I’ll respond in the current post, and will devote most … Continue reading...

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After 150 Years, the ASA Says No to p-values

March 7, 2016
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After 150 Years, the ASA Says No to p-values

Sadly, the concept of p-values and significance testing forms the very core of statistics. A number of us have been pointing out for decades that p-values are at best underinformative and often misleading. Almost all statisticians agree on this, yet they all continue to use it and, worse, teach it. I recall a few years … Continue reading...

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Quick Intro to NMF (the Method and the R Package)

March 6, 2016
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Quick Intro to NMF (the Method and the R Package)

Nonnegative matrix factorization (NMF) is a popular tool in many applications, such as image and text recognition. If you’ve ever wanted to learn a little bit about NMF, you can do so right here, in this blog post, which will summarize the (slightly) longer presentation here. The R package NMF will be used as illustration. … Continue reading...

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Innumeracy, Statistics and R

March 1, 2016
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Innumeracy, Statistics and R

A couple of years ago, when an NPR journalist was interviewing me, the conversation turned to quantitative matters. The reporter said, only half jokingly, “We journalists are innumerate and proud.” :-) Some times it shows, badly. This morning a radio reporter stated, “Hillary Clinton beat Bernie Sanders among South Carolina African-Americans by an almost 9-to-1 … Continue reading...

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50% Draft of Forthcoming Book Available

March 1, 2016
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50% Draft of Forthcoming Book Available

As I’ve mentioned here a couple of times, I am in the midst of writing a book, From Linear Models to Machine Learning: Regression and Classification, with Examples in R. As has been my practice with past books, I have now placed a 50% rough draft of the book on the Web. You will see … Continue reading...

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Some Comments on Donaho’s “50 Years of Data Science”

January 23, 2016
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Some Comments on Donaho’s “50 Years of Data Science”

An old friend recently called my attention to a thoughtful essay by Stanford statistics professor David Donaho, titled “50 Years of Data Science.” Given the keen interest these days in data science, the essay is quite timely. The work clearly shows that Donaho is not only a grandmaster theoretician, but also a statistical philosopher. The … Continue reading...

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The Generalized Method of Moments and the gmm package

December 20, 2015
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The Generalized Method of Moments and the gmm package

An almost-as-famous alternative to the famous Maximum Likelihood Estimation is the Method of Moments. MM has always been a favorite of mine because it often requires fewer distributional assumptions than MLE, and also because MM is much easier to explain than MLE to students and consulting clients. CRAN has a package gmm that does MM, … Continue reading...

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