Mathematical Statistics Lesson of the Day – Basu’s Theorem

Mathematical Statistics Lesson of the Day – Basu’s Theorem

Today’s Statistics Lesson of the Day will discuss Basu’s theorem, which connects the previously discussed concepts of minimally sufficient statistics, complete statistics and ancillary statistics.  As before, I will begin with the following set-up. Suppose that you collected data in order to estimate a parameter .  Let be the probability density function (PDF) or probability […]

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Mathematical Statistics Lesson of the Day – Basu’s Theorem

Mathematical Statistics Lesson of the Day – Basu’s Theorem

Today’s Statistics Lesson of the Day will discuss Basu’s theorem, which connects the previously discussed concepts of minimally sufficient statistics, complete statistics and ancillary statistics.  As before, I will begin with the following set-up. Suppose that you collected data in order to estimate a parameter .  Let be the probability density function (PDF) or probability […]

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United Nations gets dataviz

July 21, 2015
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The UN, as I noted before, is getting into the dataviz game. Here is an announcement about a Data Viz Challenge that has just started. Flood them with ideas! *** I am writing to invite you and your network of...

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"Models, Models Everywhere!" Brought to You by R

July 21, 2015
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"Models, Models Everywhere!" Brought to You by R

Statistical software packages sell solutions. If you go to the home page for SAS, they will tell you upfront that they sell products and solutions. They link both together under the first tab just below "The Power to Know" mantra. SPSS separates produc...

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A bad definition of statistical significance from the U.S. Department of Health and Human Services, Effective Health Care Program

July 21, 2015
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A bad definition of statistical significance from the U.S. Department of Health and Human Services, Effective Health Care Program

As D.M.C. would say, bad meaning bad not bad meaning good. Deborah Mayo points to this terrible, terrible definition of statistical significance from the Agency for Healthcare Research and Quality: Statistical Significance Definition: A mathematical technique to measure whether the results of a study are likely to be true. Statistical significance is calculated as the […] The post A bad definition of statistical significance from the U.S. Department of Health…

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Choosing a Classifier

July 21, 2015
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Choosing a Classifier

In order to illustrate the problem of chosing a classification model consider some simulated data, > n = 500 > set.seed(1) > X = rnorm(n) > ma = 10-(X+1.5)^2*2 > mb = -10+(X-1.5)^2*2 > M = cbind(ma,mb) > set.seed(1) > Z = sample(1:2,size=n,replace=TRUE) > Y = ma*(Z==1)+mb*(Z==2)+rnorm(n)*5 > df = data.frame(Z=as.factor(Z),X,Y) A first strategy is to split the dataset in two parts, a training dataset, and a testing dataset. >…

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MacBook Air battery replacement

July 21, 2015
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MacBook Air battery replacement

After four years of daily use our MacBook Air informed us that it needed a battery replacement. That's kind of nice to know, in particular as it still feels speedy and otherwise just works. A new battery isn't that expensive and according to iFixit it ...

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Parametric Inference: Karlin-Rubin Theorem

July 20, 2015
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Parametric Inference: Karlin-Rubin Theorem

A family of pdfs or pmfs $\{g(t|\theta):\theta\in\Theta\}$ for a univariate random variable $T$ with real-valued parameter $\theta$ has a monotone likelihood ratio (MLR) if, for every $\theta_2>\theta_1$, $g(t|\theta_2)/g(t|\theta_1)$ is a monotone ...

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Don’t put your whiteboard behind your projection screen

July 20, 2015
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Daniel, Andrew, and I are on our second day of teaching, and like many places, Memorial Sloan-Kettering has all their classrooms set up with a whiteboard placed directly behind a projection screen. This gives us a sliver of space to write on without pulling the screen up and down. If you have any say in […] The post Don’t put your whiteboard behind your projection screen appeared first on Statistical…

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Richard Feynman and the tyranny of measurement

July 20, 2015
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I followed a link at Steve Hsu’s blog and came to this discussion of Feyman’s cognitive style. Hsu writes that “it was often easier for [Feynman] to invent his own solution than to read through someone else’s lengthy paper” and he follows up with a story in which “Feynman did not understand the conventional formulation […] The post Richard Feynman and the tyranny of measurement appeared first on Statistical Modeling,…

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On deck this week

July 20, 2015
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Mon: Richard Feynman and the tyranny of measurement Tues: A bad definition of statistical significance from the U.S. Department of Health and Human Services, Effective Health Care Program Wed: Ta-Nehisi Coates, David Brooks, and the “street code” of journalism Thurs: Flamebait: “Mathiness” in economics and political science Fri: 45 years ago in the sister blog […] The post On deck this week appeared first on Statistical Modeling, Causal Inference, and…

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On deck for the rest of the summer and beginning of fall

July 20, 2015
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Here’s some summer reading for you. The schedule may change because of the insertion of topical material, but this is the basic plan: Richard Feynman and the tyranny of measurement A bad definition of statistical significance from the U.S. Department of Health and Human Services, Effective Health Care Program Ta-Nehisi Coates, David Brooks, and the […] The post On deck for the rest of the summer and beginning of fall…

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It is possible to not learn real causes from some A/B tests

July 20, 2015
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It is conventional wisdom that A/B testing (or in proper terms, randomized controlled experiments) is the gold standard for causal analysis, meaning if you run an A/B test, you know what caused an effect. In practice, this is not always true. Sometimes, the A/B test only provides a statistical understanding of causes but not an average Joe's understanding. Let's start with a hypothetical example in which both definitions are aligned.…

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Create a density curve with shaded tails

July 20, 2015
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Create a density curve with shaded tails

A SAS programmer wanted to plot the normal distribution and highlight the area under curve that corresponds to the tails of the distribution. For example, the following plot shows the lower decile shaded in blue and the upper decile shaded in red. An easy way to do this in SAS […] The post Create a density curve with shaded tails appeared first on The DO Loop.

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Introducing Ben Connault

July 20, 2015
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I should introduce Benjamin ("Ben") Connault, Penn's newly-hired young econometrician, arriving from Princeton any day now. We're extremely grateful to Bo Honoré, Ulrich Müller, Andriy Norets, and Chris Sims for sending him our way.Fra...

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MCMskv, Lenzerheide, 4-7 Jan., 2016 [news #1]

July 19, 2015
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MCMskv, Lenzerheide, 4-7 Jan., 2016 [news #1]

The BayesComp MCMski V [or MCMskv for short] has now its official website, once again maintained by Merrill Lietchy from Drexel University, Philadelphia, and registration is even open! The call for contributed sessions is now over, while the call for posters remains open until the very end. The novelty from the previous post is that there […]

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“17 Baby Names You Didn’t Know Were Totally Made Up”

July 19, 2015
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From Laura Wattenberg: Want to drive the baby-naming public up the wall? Tell them you’re naming your daughter Renesmee. Author Stephenie Meyer invented the name for the half-vampire child in her wildly popular Twilight series. In the story it’s simply an homage to the child’s two grandmothers, Renee and Esmé. To the traditional-minded, though, Renesmee […] The post “17 Baby Names You Didn’t Know Were Totally Made Up” appeared first…

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Predicting Titanic deaths on Kaggle

July 19, 2015
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Predicting Titanic deaths on Kaggle

Kaggle has a competition to predict who will die on the famous Titanic 'Machine Learning from Disaster''. It is placed as knowledge competition. Just up there to learn. I am late to the party, it has been been for 1 1/2 year, to end by end 2015. It is ...

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Lauryn’s back!

July 19, 2015
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Really, no snark here. She’s got some excellent tracks on the new Nina Simone tribute album. The best part’s the sample from the classic Nina song. But that’s often the case. They wouldn’t sample something if it was no good. P.S. Let me clarify: I prefer Lauryn’s version to Nina’s original. The best parts of […] The post Lauryn’s back! appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Annals of Spam

July 18, 2015
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I received the following email with subject line, “Andrew, just finished ‘Foreign language skills …'”: Andrew, Just finished http://andrewgelman.com/2010/12/24/foreign_languag/ This leads to the silliness of considering foreign language skills as a purely positional good or as a method for selecting students, while forgetting the direct benefits of being able to communicate in various ways with […] The post Annals of Spam appeared first on Statistical Modeling, Causal Inference, and Social…

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The mathematics of love

July 17, 2015
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I can't remember how I came across this (I think I saw an article about it on Metro or something), but I got intrigued by Hannah Fry's work on "The mathematics of love". So I bought the book and read it $-$ it's a fairly easy read, so I got throug...

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The statistics department Moneyball opportunity

July 17, 2015
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The statistics department Moneyball opportunity

Moneyball is a book and a movie about Billy Bean. It makes statisticians look awesome and I loved the movie. I loved it so much I’m putting the movie trailer right here: The basic idea behind Moneyball was that the Oakland Athletics were able to build a very successful baseball team on a tight budget by valuing

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Measurement is part of design

July 17, 2015
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The other day, in the context of a discussion of an article from 1972, I remarked that the great statistician William Cochran, when writing on observational studies, wrote almost nothing about causality, nor did he mention selection or meta-analysis. It was interesting that these topics, which are central to any modern discussion of observational studies, […] The post Measurement is part of design appeared first on Statistical Modeling, Causal Inference,…

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