Publications by arthur charpentier
R, Twitter and URLs
Yesterday evening, I wanted to play with Twitter, and see which websites I was using as references in my tweets, to get a Top 4 list. The first problem I got was because installing twitteR on Ubuntu is not that simple ! You have to install properly RCurl… But you before install the package in R, it is necessary to run the following line in a te...
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Linear regression from a contingency table
This morning, Benoit sent me an email, about an exercise he found in an econometric textbook, about linear regression. Consider the following dataset, Here, variable X denotes the income, and Y the expenses. The goal was to fit a linear regression (actually, in the email, it was mentioned that we should try to fit an heteroscedastic model, but l...
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Non-observable vs. observable heterogeneity factor
This morning, in the ACT2040 class (on non-life insurance), we’ve discussed the difference between observable and non-observable heterogeneity in ratemaking (from an economic perspective). To illustrate that point (we will spend more time, later on, discussing observable and non-observable risk factors), we looked at the following simple exampl...
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Monty Hall (oh no, not again)
Quite frequently, someone on the internet discovers the Monty Hall paradox, and become so enthusiastic that it becomes urgent to publish an article – or a post – about it. The latest example can be http://www.bbc.co.uk/news/magazine-24045598. I won’t blame them, I did the same a few years ago (see http://freakonometrics.hypotheses.org/776, ...
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Logistic regression and categorical covariates
A short post to get back – for my nonlife insurance course – on the interpretation of the output of a regression when there is a categorical covariate. Consider the following dataset > db = read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";") > tail(db) Y X1 X2 X3 995 1 4.801836 20.82947 A 996 1 9.867854...
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Nice tutorials to discover R
A series of tutorials, in R, by Anthony Damico. As claimed on http://twotorials.com/, “how to do stuff in r. two minutes or less, for those of us who prefer to learn by watching and listening“. So far, 000 what is r? the lingua statistica, s’il vous plaît 001 how to download and install r 002 simple shortcuts for the windows r console 0...
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ROC curves and classification
To get back to a question asked after the last course (still on non-life insurance), I will spend some time to discuss ROC curve construction, and interpretation. Consider the dataset we’ve been using last week, > db = read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";") > attach(db) The first step is to get a model. For inst...
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Regression on variables, or on categories?
I admit it, the title sounds weird. The problem I want to address this evening is related to the use of the stepwise procedure on a regression model, and to discuss the use of categorical variables (and possible misinterpreations). Consider the following dataset > db = read.table("http://freakonometrics.free.fr/db2.txt",header=TRUE,sep=";") First...
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Some heuristics about local regression and kernel smoothing
In a standard linear model, we assume that . Alternatives can be considered, when the linear assumption is too strong. Polynomial regression A natural extension might be to assume some polynomial function, Again, in the standard linear model approach (with a conditional normal distribution using the GLM terminology), parameters can be obtaine...
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Some heuristics about spline smoothing
Let us continue our discussion on smoothing techniques in regression. Assume that . where is some unkown function, but assumed to be sufficently smooth. For instance, assume that is continuous, that exists, and is continuous, that exists and is also continuous, etc. If is smooth enough, Taylor’s expansion can be used. Hence, for whi...
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