Let’s hear it for the Triangular Distribution!

July 29, 2013
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Let’s hear it for the Triangular Distribution!

Telly monster is my favourite character on Sesame Street, and a few years ago I was lucky enough to actually meet him. This morning I was delighted to find out from my resident Sesame Street expert that Telly monster is … Continue reading →

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BCEA 2.0

July 28, 2013
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BCEA 2.0

I know that updating a package too often is not quite good practice, so, given we've released BCEA 1.3-1 just about a month ago, this is way too soon to move forward. But between the last release and now, I've been doing some reading and have made some...

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More on that machine learning course

July 28, 2013
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Following up on our discussion the other day, Andrew Ng writes: Looking at the “typical” ML syllabus, I think most classes do a great job teaching the core ideas, but that there’re two recent trends in ML that are usually not yet reflected. First, unlike 10 years ago, a lot of our students are now […]The post More on that machine learning course appeared first on Statistical Modeling, Causal Inference,…

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Google’s Leaps and Bounds in Local Search

July 28, 2013
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Google’s Leaps and Bounds in Local Search

I recently wrote about the changes observed in Google's local search product on the web over the course of a year. This year, Google has rolled out some changes to their web based experience that indicate some healthy experimentation with...

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Modeling Match Results in La Liga Using a Hierarchical Bayesian Poisson Model: Part two.

July 28, 2013
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Modeling Match Results in La Liga Using a Hierarchical Bayesian Poisson Model: Part two.

In the last blog post I showed my initial attempt at modeling football results in La Liga using a Bayesian Poission model, but there was one glaring problem with the model; it did not consider the advantage of being the home team. In this post I will...

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Sunday data/statistics link roundup (7/28/13)

July 28, 2013
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An article in the Huffpo about a report claiming there is no gender bias in the hiring of physics faculty. I didn't read the paper carefully but  I definitely agree with the quote from  Prof. Dame Athene Donald that the comparison should … Continue reading →

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50 shades of gray: A research story

July 28, 2013
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This is a killer story (from Brian Nosek, Jeffrey Spies, and Matt Motyl). Part 1: Two of the present authors, Motyl and Nosek, share interests in political ideology. We were inspired by the fast growing literature on embodiment that demonstrates surprising links between body and mind (Markman & Brendl, 2005; Proffitt, 2006) to investigate embodiment […]The post 50 shades of gray: A research story appeared first on Statistical Modeling, Causal…

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Using finite differences to estimate the maximum of a time series

July 28, 2013
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Using finite differences to estimate the maximum of a time series

Finding the maximum value of a function is an important task in statistics. There are three approaches to finding a maxima: When the function is available as an analytic expression, you can use an optimization algorithm to find the maxima. For example, in the SAS/IML language, you can use any [...]

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A JAGS calculation on pattern of rain January 1906-1915 against 2003-2012

July 28, 2013
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Two weeks ago I showed rain data from six stations in Netherlands years 1906 till now. Last week I showed that frequency of days with and without rain differed between December 1906-1915 and December 2003-2012. This week I am considering the ...

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The Steep Price of Sparsity

July 27, 2013
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The Steep Price of Sparsity

The Steep Price of Sparsity We all love sparse estimators these days. I am referring to things like the lasso and related variable selection methods. But there is a dark side to sparsity. It’s what Hannes Leeb and Benedikt Potscher call the “return of the Hodges’ estimator.” Basically, any estimator that is capable of producing … … Continue reading →

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Teaching is hard

July 27, 2013
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Aleks pointed me to some posts on Gary Rubenstein’s blog, where I found this disheartening description of the teaching at a local high school. None of this is a surprise, but Rubenstein writes it vividly, and it’s interesting to see how ba...

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New Version: On the Birnbaum argument for the SLP: Slides for my JSM talk

July 27, 2013
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New Version: On the Birnbaum argument for the SLP: Slides for my JSM talk

In my latest formulation of the controversial Birnbaum argument for the strong likelihood principle (SLP), I introduce a new symbol  to represent a function from a given experiment-outcome pair, (E,z) to a generic inference implication.  This should clarify my argument (see my new paper). (E,z) InfrE(z) is to be read “the inference implication from outcome z in experiment E” […]

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Statistics takes center stage in the Independent

July 26, 2013
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Check out this really good piece over at the Independent. It talks about the rise of statisticians as rockstars, naming Hans Rosling, Nate Silver, and Chris Volinsky among others. I think that those guys are great and deserve all the … Continue reading →

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Evolve your own beats: automatically generating music via algorithms

July 26, 2013
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Evolve your own beats: automatically generating music via algorithms

Update: you can find the next post in this series here. I recently went to an excellent music meetup where people spoke about the intersection of music and technology. One speaker in particular talked about how music is now being generated by computer. Music has always fascinated me. It can make us feel emotions in a way few media can. Sadly, I have always been unable to play an instrument…

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“The Inside Story Of The Harvard Dissertation That Became Too Racist For Heritage”

July 26, 2013
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Mark Palko points me to a news article by Zack Beauchamp on Jason Richwine, the recent Ph.D. graduate from Harvard’s policy school who left the conservative Heritage Foundation after it came out that his Ph.D. thesis was said to be all about the low IQ’s of Hispanic immigrants. Heritage and others apparently thought this association […]The post “The Inside Story Of The Harvard Dissertation That Became Too Racist For Heritage”…

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How to choose parameters so that a distribution has a specified mean and variance

July 26, 2013
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How to choose parameters so that a distribution has a specified mean and variance

The truncated normal distribution TN(μ, σ, a, b) is the distribution of a normal random variable with mean μ and standard deviation σ that is truncated on the interval [a, b]. I previously blogged about how to implement the truncated normal distribution in SAS. A friend wanted to simulate data [...]

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Evolve your own beats: automatically generating music via algorithms

July 26, 2013
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Evolve your own beats: automatically generating music via algorithms

Update: you can find the next post in this series here. I recently went to an excellent music meetup where people spoke about the intersection of music and technology. One speaker in particular talked about how music is now being generated by computer. Music has always fascinated me. It can make us feel emotions in a way few media can. Sadly, I have always been unable to play an instrument…

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What should be in a machine learning course?

July 26, 2013
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What should be in a machine learning course?

Nando de Freitas writes: We’re designing two machine learning (ML) courses at Oxford (introductory and advanced ML). In doing this, we have many questions and wonder what your thoughts are on the following: - Which do you think are the key optimization papers/ideas that should be covered. - Which topics do you think are coolest […]The post What should be in a machine learning course? appeared first on Statistical Modeling,…

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Bayes-respecting experimental design and other things

July 25, 2013
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Bayes-respecting experimental design and other things

Dan Lakeland writes: I have some questions about some basic statistical ideas and would like your opinion on them: 1) Parameters that manifestly DON’T exist: It makes good sense to me to think about Bayesian statistics as narrowing in on the value of parameters based on a model and some data. But there are cases […]The post Bayes-respecting experimental design and other things appeared first on Statistical Modeling, Causal Inference,…

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Matching Madness: Causal Inference in Political Methodology

July 25, 2013
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Matching Madness: Causal Inference in Political Methodology

If the 2013 Methods Meetings are any indication, political methodologists really want to talk about causal inference. Four panels in the conference program actually have the term “causal inference” in their title—indeed, the word “causal” actually appears 13 times in the program—and at least two more panels were directly about how to draw causal inferences […]

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Know your data 13: no-tracking technologies

July 25, 2013
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I have been highlighting some cases of analysts doing a masterful job in taking apart claims made in the media. Here is another example from Bruce Schneier, the national security expert. In June 2013, a Who’s Who list of telecom and technology companies, such as Verizon, AT&T, Google and Facebook, was busted for passing customer data to the National Security Agency in its domestic surveillance program. This episode invites scrutiny…

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Different ways to get memory consumption or lessons learned from “memory_profiler“

July 24, 2013
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Different ways to get memory consumption or lessons learned from “memory_profiler“

As part of the development of memory_profiler I've tried several ways to get memory usage of a program from within Python. In this post I'll describe the different alternatives I've tested. The psutil library psutil is a python library that provides an...

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Too Good To Be True: The Scientific Mass Production of Spurious Statistical Significance

July 24, 2013
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Are women three times more likely to wear red or pink when they are most fertile? No, probably not. But here’s how hardworking researchers, prestigious scientific journals, and gullible journalists have been fooled into believing so. The paper I’ll be talking about appeared online this month in Psychological Science, the flagship journal of the Association […]The post Too Good To Be True: The Scientific Mass Production of Spurious Statistical Significance…

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