Posts Tagged ‘ Simulation ’

simulation, an ubiquitous tool

July 10, 2012
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simulation, an ubiquitous tool

(This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers) After struggling for quite a walk on that AMSI public lecture talk, and dreading its loss with the problematic Macbook, I managed to complete a first draft last night in Adelaide, downloading a final set of images from the Web...

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Simulating Euro 2012

June 11, 2012
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Simulating Euro 2012

Why settle for just one realisation of this year’s UEFA Euro when you can let the tournament play out 10,000 times in silico? Since I already had some code lying around from my submission to the Kaggle hosted 2010 Take on the Quants challenge, I figured I’d recycle it for the Euro this year. The

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\STATE [algorithmic package]

June 7, 2012
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\STATE [algorithmic package]

I fought with my LαTεX compiler this morning as it did not want to deal with my code: looking on forums for incompatibilities between beamer and algorithmic, and adding all kinds of packages, to no avail. Until I realised one \STATE was missing: (This is connected with my AMSI public lecture on simulation, obviously!) Filed

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ABC+EL=no D(ata)

May 27, 2012
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ABC+EL=no D(ata)

It took us a loooong while but we finally ended up completing a paper on ABC using empirical likelihood (EL) that was started by me listening to Brunero Liseo’s tutorial in O’Bayes-2011 in Shanghai… Brunero mentioned empirical likelihood as a semi-parametric technique w/o much Bayesian connections and this got me thinking

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R, Julia and genome wide selection

April 24, 2012
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R, Julia and genome wide selection

— “You are a pussy” emailed my friend. — “Sensu cat?” I replied. — “No. Sensu chicken” blurbed my now ex-friend. What was this about? He read my post on R, Julia and the shiny new thing, which prompted him … Continue reading →

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Insights into Quantile Regression from Arthur Charpentier

April 24, 2012
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Insights into Quantile Regression from Arthur Charpentier

At this Monday’s Montreal R User Group meeting, Arthur Charpentier gave an interesting talk on the subject of quantile regression. One of the main messages I took away from the workshop was that quantile regression can be used to determine if extreme events are becoming more extreme. The example given was hurricane intensity since 1978.

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Instrumental Variables without Traditional Instruments

April 14, 2012
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Instrumental Variables without Traditional Instruments

Typically, regression models in empirical economic research suffer from at least one form of endogeneity bias. The classic example is economic returns to schooling, where researchers want to know how much increased levels of education affect income. Estimation using a simple linear model, regressing income on schooling, alongside a bunch of control variables, will typically

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Monty Hall Simulations

March 20, 2012
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Using R I'm doing a lot of simulations of the classic monty hall-problem and plotting multiple simulations using ggplot2.

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π Day Special! Estimating π using Monte Carlo

March 14, 2012
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π Day Special! Estimating π using Monte Carlo

In honour of π day (03.14 – can’t wait until 2015~) , I thought I’d share this little script I wrote a while back for an introductory lesson I gave on using Monte Carlo methods for integration. The concept is simple – we can estimate the area of an object which is inside another object

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another X’idated question

February 23, 2012
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another X’idated question

An X’idated reader of Monte Carlo Statistical Methods had trouble with our Example 3.13, the very one our academic book reviewer disliked so much as to “diverse a 2 star”. The issue is with computing the integral when f is the Student’s t(5) distribution density. In our book, we compare a few importance sampling solutions,

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