1286 search results for "Latex"

Generating Labels for Supervised Text Classification using CAT and R

February 4, 2013
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The explosion in the availability of text has opened new opportunities to exploit text as data for research. As Justin Grimmer and Brandon Stewart discuss in the above paper, there are a number of approaches to reducing human text to … Continue reading →

Proposed techniques for communicating the amount of information contained in a statistical result

February 4, 2013
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$Proposed techniques for communicating the amount of information contained in a statistical result$

A couple of weeks ago, I posted about how much we can expect to learn about the state of the world on the basis of a statistical significance test. One way of framing this question is: if we’re trying to come to scientific conclusions on the basis of statistical results, how much can we update

A random walk ? What else ?

February 2, 2013
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Consider the following time series, What does it look like ? I know, this is a stupid game, but I keep using it in my time series courses. It does look like a random walk, doesn’t it ? If we use Philipps-Perron test, yes, it does, > PP.test(x) Phillips-Perron Unit Root Test data: x Dickey-Fuller = -2.2421, Truncation lag parameter = 6,...

A slightly different introduction to R, part III

February 2, 2013
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I think you’ve noticed by now that a normal interactive R session is quite messy. If you don’t believe me, try playing around for a while and then give the history() command, which will show you the commands you’ve typed. If you’re anything like me, a lot of them are malformed attempts that generated some

Overdispersion with different exposures

February 1, 2013
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In actuarial science, and insurance ratemaking, taking into account the exposure can be a nightmare (in datasets, some clients have been here for a few years – we call that exposure – while others have been here for a few months, or weeks). Somehow, simple results because more complicated to compute just because we have to take into account...

Bayesian model choice for the Poisson model

February 1, 2013
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$Bayesian model choice for the Poisson model$

Following Arthur Charpentier‘s example, I am going to try to post occasionally on material covered during my courses, in the hope that it might be useful to my students, but also to others. In the second practical of the Bayesian Case Studies course, we looked at Bayesian model choice and basic Monte Carlo methods, looking

Regression on categorical variables

January 30, 2013
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$N_{x,t}\sim\mathcal{P}(E_{x,t}\cdot \exp[\alpha_x+\beta_x \kappa_t + \gamma_x \delta_{t-x}])$

This morning, Stéphane asked me tricky question about extracting coefficients from a regression with categorical explanatory variates. More precisely, he asked me if it was possible to store the coefficients in a nice table, with information on the variable and the modality (those two information being in two different columns). Here is some code I did to produce the...

The magic empty bracket

January 30, 2013
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$The magic empty bracket$

I have been working with R for some time now, but once in a while, basic functions catch my eye that I was not aware of… For some project I wanted to transform a correlation matrix into a covariance matrix. Now, since cor2cov does not exist, I thought about “reversing” the cov2cor function (stats:::cov2cor). Inside

The law of small numbers

January 28, 2013
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$N$

In insurance, the law of large numbers (named loi des grands nombres initially by Siméon Poisson, see e.g. http://en.wikipedia.org/…) is usually mentioned to legitimate large portfolios, because of pooling and diversification: the larger the pool, the more ‘predictable’ the losses will be (in a given period). Of course, under standard statistical assumption, namely finite expected value, and independence (see http://freakonometrics.blog.free.fr/…....

My template for controlling publication quality figures

January 28, 2013
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The following is a template that I usually start with when producing figures for publication. It allows me to control:The overall size of the figure (in inches) (WIDTH, HEIGHT)The layout of figure subplots (using the layout() function) (LO)The resoluti...