# Posts Tagged ‘ statistics ’

## p-value vs Bayes

September 30, 2014
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p-value and Bayes are the two hottest words in Statistics. Actually I still can not get why the debate between frequentist  statistics and Bayesian statistics can last so long. What is the essence arguments behind it? (Any one can help me with this?) In my point of view, they are just two ways for solving […]

## R package for Computational Actuarial Science

September 29, 2014
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A webpage for the book is now hosted on http://cas.uqam.ca/ So far, it is a very basic page, but information regarding the package can be found there. For instance, to install the package, with all the datasets, the R code is > install.packages("CASdatasets", repos = "http://cas.uqam.ca/pub/R/") The reference manual provides a description of all datasets.

## future of computational statistics

September 28, 2014
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I am currently preparing a survey paper on the present state of computational statistics, reflecting on the massive evolution of the field since my early Monte Carlo simulations on an Apple //e, which would take a few days to return a curve of approximate expected squared error losses… It seems to me that MCMC is […]

## Should a “Fictionfactory” peepshow be barred from a festival on “Truth and Reality”? Diederik Stapel says no (rejected post)

September 28, 2014
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So I hear that Diederik Stapel is the co-author of a book Fictionfactory (in Dutch,with a novelist, Dautzenberg)[i], and of what they call their “Fictionfactory peepshow”, only it’s been disinvited at the last minute from a Dutch festival on“truth and reality” (due to have run 9/26/14), and all because of Stapel’s involvement. Here’s an excerpt from an article in last […]

## A little something while I’m away

September 26, 2014
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Note: This is cross-posted to both my blogs. I have been on vacation. Regular posts will resume next week. Before then, here is a little something for you. ASA News recently asked me to describe "a day in the life...

## Estimating Generalization Error with the PRESS statistic

September 25, 2014
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As we’ve mentioned on previous occasions, one of the defining characteristics of data science is the emphasis on the availability of “large” data sets, which we define as “enough data that statistical efficiency is not a concern” (note that a “large” data set need not be “big data,” however you choose to define it). In […] Related posts: Don’t use correlation to track prediction performance Correlation and R-Squared My Favorite…

## Multiple Tests, an Introduction

September 24, 2014
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$X_{i,t}$

Last week, a student asked me about multiple tests. More precisely, she ran an experience over – say – 20 weeks, with the same cohort of – say – 100 patients. An we observe some size=100 nb=20 set.seed(1) X=matrix(rnorm(size*nb),size,nb) (here, I just generate some fake data). I can visualize some trajectories, over the 20 weeks, library(RColorBrewer) cl1=brewer.pal(12,"Set3")[12] cl2=brewer.pal(8,"Set2")[2:7] cl=c(cl1,cl2) boxplot(X) for(i in 1:length(cl)){ lines(1:20,X[i,],type="b",col=cl[i]) }   She wanted to compare…

## a weird beamer feature…

September 23, 2014
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As I was preparing my slides for my third year undergraduate stat course, I got a weird error that got a search on the Web to unravel: which was related with a fragile environment but not directly the verbatim part: the reason for the bug was that the \end{frame} command did not have a line […]

## Factors are not first-class citizens in R

September 23, 2014
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The primary user-facing data types in the R statistical computing environment behave as vectors. That is: one dimensional arrays of scalar values that have a nice operational algebra. There are additional types (lists, data frames, matrices, environments, and so-on) but the most common data types are vectors. In fact vectors are so common in R […] Related posts: R has some sharp corners R minitip: don’t use data.matrix when you…

## G.A. Barnard: The Bayesian “catch-all” factor: probability vs likelihood

September 23, 2014
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Today is George Barnard’s birthday. In honor of this, I have typed in an exchange between Barnard, Savage (and others) on an important issue that we’d never gotten around to discussing explicitly (on likelihood vs probability). Please share your thoughts. The exchange is from pp 79-84 (of what I call) “The Savage Forum” (Savage, 1962)[i]  ♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠♠ BARNARD:…Professor Savage, as […]