Sunday data/statistics link roundup (1/5/14)

January 5, 2014
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Sunday data/statistics link roundup (1/5/14)

If you haven't seen lolmythesis it is pretty incredible. 1-2 line description of thesis projects. I think every student should be required to make one of these up before they defend. The best I could come up with for mine … Continue reading →

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Your 2014 wishing well….

January 4, 2014
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Your 2014 wishing well….

A reader asks how I would complete the following sentence: I wish that new articles* written in 2014 would refrain from_______.   Here are my quick answers, in no special order: (a) rehearsing the howlers of significance tests and other frequentist statistical methods; (b) misinterpreting p-values, ignoring discrepancy assessments (and thus committing fallacies of rejection […]

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Machine Learning Lesson of the Day – Supervised and Unsupervised Learning

Machine Learning Lesson of the Day – Supervised and Unsupervised Learning

The 2 most commonly used and studied categories of machine learning are supervised learning and unsupervised learning. In supervised learning, there is a target variable, , and a set of predictor variables, .  The goal is to use  to predict .  Supervised learning is synonymous with predictive modelling, but the latter term does not connote […]

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Repost: Prediction: the Lasso vs. just using the top 10 predictors

January 4, 2014
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Repost: Prediction: the Lasso vs. just using the top 10 predictors

Editor's note: This is a previously published post of mine from a couple of years ago (!). I always thought about turning it into a paper. The interesting idea (I think) is how the causal model matters for whether the … Continue reading →

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Applied Statistics Lesson of the Day – Basic Terminology in Experimental Design #1

Applied Statistics Lesson of the Day – Basic Terminology in Experimental Design #1

Experiment: A procedure to determine the causal relationship between 2 variables – an explanatory variable and a response variable.  The value of the explanatory variable is changed, and the value of the response variable is observed for each value of the explantory variable. An experiment can have 2 or more explanatory variables and 2 or […]

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“Dogs are sensitive to small variations of the Earth’s magnetic field”

January 4, 2014
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“Dogs are sensitive to small variations of the Earth’s magnetic field”

Two different people pointed me to this article by Vlastimil Hart et al. in the journal Frontiers in Zoology: It is for the first time that (a) magnetic sensitivity was proved in dogs, (b) a measurable, predictable behavioral reaction upon natural MF fluctuations could be unambiguously proven in a mammal, and (c) high sensitivity to […]The post “Dogs are sensitive to small variations of the Earth’s magnetic field” appeared first…

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Multivariate Archimax copulas

January 4, 2014
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Our paper, written jointly also with Anne-Laure Fougères, Christian Genest and Johanna Nešlehová, entitled Multivariate Archimax Copulas, should appear some day in the Journal of Multivariate Analysis. “A multivariate extension of the bivariate class of Archimax copulas was recently proposed by Mesiar & Jagr (2013), who asked under which conditions it holds. This paper answers their question and provides a stochastic representation of multivariate Archimax copulas. A few basic properties of these copulas are…

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Le Monde puzzle 847 in Julia

January 4, 2014
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This week I wanted to play around with Julia and exporting the results. I found http://xianblog.wordpress.com/2013/12/29/le-monde-puzzle-847/ to be just the right size to play around with.CodeA function to check if a triplet has the desir...

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Imprecise machines mess with me

January 3, 2014
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Just a little while ago, I showed an example of imprecise algorithms and how it causes incorrect historical facts to be promulgated. The point is not that algorithms are scary things but that we should not confuse efficiency with accuracy (or truth). So this past week, I have another encounter with imprecise machines, and this time, it's personal. *** If you go to Amazon right now, and search for my…

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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #17)

January 3, 2014
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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #17)

We are a few days from the start, here are the latest items of information for the participants: The shuttle transfer on January 5th, from Geneva Airport to Chamonix lasts 1 hour 30 minutes. At your arrival in the airport , follow the “Swiss Exit”. After the customs, the bus driver (handling a sign “MCMC’Ski […]

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Booze: Been There. Done That.

January 3, 2014
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Booze: Been There. Done That.

Our research assistants have unearthed the following guest column by H. L. Mencken which appeared in the New York Times of 5 Nov 1933, the date at which Prohibition ended in the United States. As a public service we are reprinting it here. I’m particularly impressed at how the Sage of Baltimore buttressed his article […]The post Booze: Been There. Done That. appeared first on Statistical Modeling, Causal Inference, and…

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The Supreme Court takes on Pollution Source Apportionment…and Realizes It’s Hard

January 3, 2014
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Recently, the U.S. Supreme Court heard arguments in the cases EPA v. EME Homer City Generation and American Lung Association v EME Homer City Generation. SCOTUSblog has a nice summary of the legal arguments, for the law buffs out there. The basic problem is … Continue reading →

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Numbersense in education: gaming the statistics, cheating scandals, and more

January 3, 2014
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My friend Kate alerted me to the notable New York Times story on academic fraud at the University of North Carolina (Chapel Hill). Phantom courses have been created to provide students with A grades, in some cases, for the benefit of athletes. This story fits the larger pattern of fraudulent practices across the education sector, which is the subject of Chapter 1 of Numbersense (link). The story about law school…

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Error Statistics Philosophy: 2013

January 3, 2014
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Error Statistics Philosophy: 2013

Error Statistics Philosophy: 2013 Organized by Nicole Jinn & Jean Anne Miller*  January 2013 (1/2) Severity as a ‘Metastatistical’ Assessment (1/4) Severity Calculator (1/6) Guest post: Bad Pharma? (S. Senn) (1/9) RCTs, skeptics, and evidence-based policy (1/10) James M. Buchanan (1/11) Aris Spanos: James M. Buchanan: a scholar, teacher and friend (1/12) Error Statistics Blog: Table of Contents (1/15) Ontology & Methodology: Second call […]

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Lab: Tremors (Introduction to Statistical Computing)

January 2, 2014
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In which we use reading a catalog of earthquakes as a way to practice extracting data from texts. Assignment, ckm.csv data set. Introduction to Statistical Computing

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Lab: Scrape the Rich (Introduction to Statistical Computing)

January 2, 2014
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In which we practice extracting data from text, to learn about our betters. Assignment; files: rich-1.html, rich-2.html, rich-3.html, rich-4.html (This assignment ripped off from Vince Vu, with permission.) Introduction to Statistical Computing

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Homework: A Maze of Twisty Little Passages (Introduction to Statistical Computing)

January 2, 2014
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Homework 10: In which we build a little web-crawler to calculate page-rank (the hard way), so as to practice working with text, regular expressions, and Markov chains. Supplied code, which may or may not contain deliberate bugs. (This assignment ri...

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Lab: Baseball Salaries (Introduction to Statistical Computing)

January 2, 2014
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In which America's true past-time proves to be wrestling with relational databases. Assignment, database (large!) Introduction to Statistical Computing

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Homework: Several Hundred Degrees of Separation (Introduction to Statistical Computing)

January 2, 2014
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Homework 10: in which we refine our web-crawler from the previous assignment, by way of further working with regular expressions, and improving our estimates of page-rank. (This assignment ripped off from Vince Vu, with permission.) Introduction...

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Simulation V: Matching Simulation Models to Data (Introduction to Statistical Computing)

January 2, 2014
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\[ \newcommand{\Expect}[1]{\mathbb{E}\left[ #1 \right]} \DeclareMathOperator*{\argmin}{argmin} \] (My notes for this lecture are too incomplete to be worth typing up, so here's the sketch.) Methods, Models, Simulations Statistical methods try t...

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Computing for Statistics (Introduction to Statistical Computing)

January 2, 2014
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(My notes from this lecture are too fragmentary to post; here's the sketch.) What should you remember from this class? Not: my mistakes (though remember that I made them). Not: specific packages and ways of doing things (those will change). Not: t...

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36-350, Fall 2013: Self-Evaluation and Lessons Learned (Introduction to Statistical Computing)

January 2, 2014
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This was not one of my better performances as a teacher. I felt disorganized and unmotivated, which is a bit perverse, since it's the third time I've taught the class, and I know the material very well by now. The labs were too long, and my attempts...

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Simulation IV: Quantifying Uncertainty with Simulations (Introduction to Statistical Computing)

January 2, 2014
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(My notes for this lecture are too fragmentary to write up properly; here's the sketch.) Two forms of statistical uncertainty: (I) How much would our answers change if the data were different? (II) How diverse are the answers which don't make use hat...

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