Articles by arthur charpentier

Playing with Leaflet (and Radar locations)

September 30, 2015 | arthur charpentier

Yesterday, my friend Fleur did show me some interesting features of the leaflet package, in R. library(leaflet) In order to illustrate, consider locations of (fixed) radars, in several European countries. To get the data, use download.file("http://carte-gps-gratuite.fr/radars/zones-de-danger-destinator.zip","radar.zip") unzip("radar.zip")   ext_...
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Computational Time of Predictive Models

September 25, 2015 | arthur charpentier

Tuesday, at the end of my 5-hour crash course on machine learning for actuaries, Pierre asked me an interesting question about computational time of different techniques. I’ve been presenting the philosophy of various algorithm, but I forgot to mention computational time. I wanted to try several classification algorithms on ...
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Convergence and Asymptotic Results

September 24, 2015 | arthur charpentier

Last week, in our mathematical statistics course, we’ve seen the law of large numbers (that was proven in the probability course), claiming that given a collection  of i.i.d. random variables, with To visualize that convergence, we can use __ m=100 __ mean_samples=function(n=10){ + X=matrix(rnorm(n*... [Read more...]

Minimalist Maps

September 5, 2015 | arthur charpentier

This week, I mentioned a series of maps, on Twitter, some minimalist maps http://t.co/YCNPf3AR9n (poke @visionscarto) pic.twitter.com/Ip9Tylsbkv — Arthur Charpentier (@freakonometrics) 2 Septembre 2015 Friday evening, just before leaving the office to pick-up the kids after their first week back in class, Matthew Champion (...
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On NCDF Climate Datasets

September 3, 2015 | arthur charpentier

Mid november, a nice workshop on big data and environment will be organized, in Argentina, We will talk a lot about climate models, and I wanted to play a little bit with those data, stored on http://dods.ipsl.jussieu.fr/mc2ipsl/. Since Ewen (aka @3wen) has been working ...
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“A 99% TVaR is generally a 99.6% VaR”

August 29, 2015 | arthur charpentier

Almost 6 years ago, I posted a brief comment on a sentence I found surprising, by that time, discovered in a report claiming that the expected shortfall […] at the 99 % level corresponds quite closely to the […] value-at-risk at a 99.6% level which was inspired by a remark in Swiss Experience report, expected shortfall […] ... [Read more...]

Pricing Game

August 22, 2015 | arthur charpentier

In November, with Romuald Elie and Jérémie Jakubowicz, we will organize a session during the 100% Actuaires day, in Paris, based on a “pricing game“. We provide two datasets, (motor insurance, third party claims), with 2  years of experience, and 100,000 policies. Each ‘team’ has to submit premium proposal for 36,000 potential ... [Read more...]

Computing AIC on a Validation Sample

July 29, 2015 | arthur charpentier

This afternoon, we’ve seen in the training on data science that it was possible to use AIC criteria for model selection. __ library(splines) __ AIC(glm(dist ~ speed, data=train_cars, family=poisson(link="log"))) [1] 438.6314 __ AIC(glm(dist ~ speed, data=train_cars, family=poisson(link="identity"))) [1] 436.3997 __ AIC(glm(dist ~ bs(...
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Choosing a Classifier

July 21, 2015 | arthur charpentier

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=...
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An Update on Boosting with Splines

July 2, 2015 | arthur charpentier

In my previous post, An Attempt to Understand Boosting Algorithm(s), I was puzzled by the boosting convergence when I was using some spline functions (more specifically linear by parts and continuous regression functions). I was using __ library(splines) __ fit=lm(y~bs(x,degree=1,df=3),data=df) The problem ... [Read more...]

An Attempt to Understand Boosting Algorithm(s)

June 26, 2015 | arthur charpentier

Tuesday, at the annual meeting of the French Economic Association, I was having lunch Alfred, and while we were chatting about modeling issues (econometric models against machine learning prediction), he asked me what boosting was. Since I could not be very specific, we’ve been looking at wikipedia page. Boosting ... [Read more...]

‘Variable Importance Plot’ and Variable Selection

June 17, 2015 | arthur charpentier

Classification trees are nice. They provide an interesting alternative to a logistic regression.  I started to include them in my courses maybe 7 or 8 years ago. The question is nice (how to get an optimal partition), the algorithmic procedure is nice (the trick of splitting according to one variable, and only ...
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p-hacking, or cheating on a p-value

June 11, 2015 | arthur charpentier

Yesterday evening, I discovered some interesting slides on False-Positives, p-Hacking, Statistical Power, and Evidential Value, via @UCBITSS ‘s post on Twitter. More precisely, there was this slide on how cheating (because that’s basically what it is) to get a ‘good’ model (by targeting the p-value) As mentioned by @david_...
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Data Science: from Small to Big Data

May 29, 2015 | arthur charpentier

This Tuesday, I will be in Leuven (in Belgium) at the ACP meeting to give  a talk on Data Science: from Small to Big Data. The talk will take place in the Faculty Club from 6 till 8 pm. Slides could be found online (with animated pictures). As usual, comments are welcome.
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Copulas and Financial Time Series

May 12, 2015 | arthur charpentier

I was recently asked to write a survey on copulas for financial time series. The paper is, so far, unfortunately, in French, and is available on https://hal.archives-ouvertes.fr/. There is a description of various models, including some graphs and statistical outputs, obtained from read data. To illustrate, I’... [Read more...]
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