## Everything I need to know about Bayesian statistics, I learned in eight schools.

January 21, 2014
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This post is by Phil. I’m aware that there are some people who use a Bayesian approach largely because it allows them to provide a highly informative prior distribution based subjective judgment, but that is not the appeal of Bayesian methods for a lot of us practitioners. It’s disappointing and surprising, twenty years after my initial experiences, […]The post Everything I need to know about Bayesian statistics, I learned in eight schools.…

## Causal Autoregressive Time Series

January 21, 2014
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$AR(1)$

In the MAT8181 graduate course on Time Series, we will discuss (almost) only causal models. For instance, with , with some white noise , those models are obtained when . In that case, we’ve seen that was actually the innovation process, and we can write which is actually a mean-square convergent series (using simple Analysis arguments on series). From that expression, we can easily see that is stationary, since (which does…

## Intuitively, it is clear that it is obvious that any idiot can see

January 21, 2014
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In technical writing, three terms/phrases not to be used: Intuitively, ... It is clear that ... It is obvious that ... Just as well you could write Any idiot can plainly see ... These phrases may be true for you, the writer. However, the reader won't h...

## The Johns Hopkins Data Science Specialization on Coursera

January 21, 2014
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We are very proud to announce the the Johns Hopkins Data Science Specialization on Coursera. You can see the official announcement from the Coursera folks here. This is the main reason Simply Statistics has been a little quiet lately. The … Continue reading →

## Visualizing Autoregressive Time Series

January 21, 2014
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$AR(1)$

In the MAT8181 graduate course on Time Series, we started discussing autoregressive models. Just to illustrate, here is some code to plot  – causal – process, > graphar1=function(phi){ + nf <- layout(matrix(c(1,1,1,1,2,3,4,5), 2, 4, byrow=TRUE), respect=TRUE) + e=rnorm(n) + X=rep(0,n) + for(t in 2:n) X[t]=phi*X[t-1]+e[t] + plot(X[1:6000],type="l",ylab="") + abline(h=mean(X),lwd=2,col="red") + abline(h=mean(X)+2*sd(X),lty=2,col="red") + abline(h=mean(X)-2*sd(X),lty=2,col="red") + u=seq(-1,1,by=.001) + plot(0:1,0:1,col="white",xlab="",ylab="",axes=FALSE,ylim=c(-2,2),xlim=c(-2.5,2.5)) + polygon(c(u,rev(u)),c(sqrt(1-u^2),rev(-sqrt(1-u^2))),col="light yellow") + abline(v=0,col="grey") + abline(h=0,col="grey") + points(1/phi,0,pch=19,col="red",cex=1.3) + plot(0:1,0:1,col="white",xlab="",ylab="",axes=FALSE,ylim=c(-.2,.2),xlim=c(-1,1)) + axis(1) +…

## The Commissar for Traffic presents the latest Five-Year Plan

January 21, 2014
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What do Paul Samuelson and the U.S. Department of Transportation have in common? Phil Price points us to this news article by Clark Williams-Derry: As the State Smart Transportation Initiative at the University of Wisconsin points out, the US Department of Transportation has been making the virtually identical vehicle travel forecasts for well over a […]The post The Commissar for Traffic presents the latest Five-Year Plan appeared first on Statistical…

## Where are the millionaires? Where’s the news?

January 21, 2014
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The financial media, ranging from Wall Street Journal to Zero Hedge, blogged about the geographical distribution of U.S. millionaires. The stories came with a map, and in the case of the latter, two data tables ranked by ascending and descending...

## The best articles of 2013: Twelve posts from The DO Loop that merit a second look

January 21, 2014
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I began 2014 by compiling a list of 13 popular articles from my blog in 2013. Although this "People's Choice" list contains many articles that I am proud of, it did not include all of my favorites, so I decided to compile an "Editor's Choice" list. The blog posts on [...]

## Fun with the Raspberry Pi

January 21, 2014
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Since Christmas I have been playing around with a Raspberry Pi. It is certainly not the fastest computer, but what a great little toy! Here are a few experiences and online resources that I found helpful.SetupInitially I connected the Raspberry Pi via ...

## Peer Review, Part 2: How It Works

January 21, 2014
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Peer review is one of the central pillars of academic publishing. But how does it actually work? What is blind review, and what is it good for? This part will answer those questions, and then tell you how to be a good reviewer yourself. The Process The basic process is this: you have some work […]

## Estimating a nonlinear time series model in R

January 21, 2014
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There are quite a few R packages available for nonlinear time series analysis, but sometimes you need to code your own models. Here is a simple example to show how it can be done. The model is a first order threshold autoregression:     where is a Gaussian white noise series with variance . The following function will generate some random data from this model. simnlts <- function(n, alpha, beta,…

## Thanks a Milllion!

January 21, 2014
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So,.......... by reading this post you'll assist in pushing the total number of page-views for this blog, since its inception in 2011, above the 1 Million mark. Thanks for your interest, support, and questions.It's been a blast!© 2014, David E. Giles

## Mailing List Degree-of-Difficulty Difficulty

January 20, 2014
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The Difficulty with Difficult Questions Andrew’s commented during our Stan meetings that he’s observed that when a user sends an easy question to a mailing list, it gets answered right away, whereas difficult questions often languish with no answers. These difficult questions usually come from power users with real issues, whereas the simple questions are […]The post Mailing List Degree-of-Difficulty Difficulty appeared first on Statistical Modeling, Causal Inference, and Social…

## Bayesian First Aid: Binomial Test

January 20, 2014
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The binomial test is arguably the conceptually simplest of all statistical tests: It has only one parameter and an easy to understand distribution for the data. When introducing null hypothesis significance testing it is puzzling that the binomial te...

## Statistical modeling: two ways to see the world.

January 20, 2014
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This a machine-learning-vs-traditional-statistics kind of blog post inspired by Leo Breiman's "Statistical Modeling: The Two Cultures". If you're like: "I had enough of this machine learning vs. statistics discussion,  BUT I would love to see beau...

## Rectangular Integration (a.k.a. The Midpoint Rule) – Conceptual Foundations and a Statistical Application in R

$Rectangular Integration (a.k.a. The Midpoint Rule) – Conceptual Foundations and a Statistical Application in R$

Introduction Continuing on the recently born series on numerical integration, this post will introduce rectangular integration.  I will describe the concept behind rectangular integration, show a function in R for how to do it, and use it to check that the distribution actually integrates to 1 over its support set.  This post follows from my […]

## The AAA Tranche of Subprime Science

January 20, 2014
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In our new ethics column for Chance, Eric Loken and I write about our current favorite topic: One of our ongoing themes when discussing scientific ethics is the central role of statistics in recognizing and communicating uncer- tainty. Unfortunately, statistics—and the scientific process more generally—often seems to be used more as a way of laundering […]The post The AAA Tranche of Subprime Science appeared first on Statistical Modeling, Causal Inference,…

## Statistics meets rhetoric: A text analysis of "I Have a Dream" in R

January 20, 2014
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Today, we celebrate the would-be 85th birthday of Martin Luther King, Jr., a man remembered for pioneering the civil rights movement through his courage, moral leadership, and oratory prowess. This post focuses on his most famous speech, I Have a Drea...

## Peer Review, Part 1: Quilt Plots

January 20, 2014
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What is peer review? How does it work? And is it really as flawed as people claim it is? In this little series, I will talk about all that, and then end up arguing that peer review does, in fact, work – at least in visualization. But first an example where it didn’t. A paper […]

## Sunday data/statistics link roundup (1/19/2014)

January 20, 2014
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Tesla is hiring a data scientist. That is all. I'm not sure I buy the idea that Python is taking over for R among people who actually do regular data science.  I think it is still context dependent. A huge … Continue reading →

## Faire parler les chiffres… n’importe comment

January 19, 2014
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Cette fin de semaine, Martin Grandjean a mis en ligne un billet intéressant sur son blog, sur l’utilisation des statistiques (dans un but de propagande). L’exercice n’est pas nouveau, mais Martin soulève des questions, malheureusement importantes et complexes. Dans un paragraphe, intitulé “faire parler les chiffres… n’importe comment” (que j’ai repris comme titre, j’avoue avoir hésité avec “with great power comes great responsibility“), on retrouve l’analyse (rapide) d’un graphique, présenté ci-dessous.…

## “The British amateur who debunked the mathematics of happiness”

January 19, 2014
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Andrew Anthony tells the excellent story of how Nick Brown, Alan Sokal, and Harris Friedman shot down some particularly silly work in psychology. (“According to the graph, it all came down to a specific ratio of positive emotions to negative emotions. If your ratio was greater than 2.9013 positive emotions to 1 negative emotion you […]The post “The British amateur who debunked the mathematics of happiness” appeared first on Statistical…

January 19, 2014
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Nassim Nicholas Taleb recently wrote an article advocating the abandonment of the use of standard deviation and advocating the use of mean absolute deviation. Mean absolute deviation is indeed an interesting and useful measure- but there is a reason that standard deviation is important even if you do not like it: it prefers models that […] Related posts: Don’t use correlation to track prediction performance What does a generalized linear…