# 998 search results for "latex"

## R 3.1.1 is released (and how to quickly update it on Windows OS)

July 10, 2014
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R 3.1.1 (codename “Sock it to Me“) was released today! You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below. Upgrading to R 3.1.1 on Windows If you are using Windows you can easily upgrade to the latest version of R using the

## Mathematical functions on non-numbers?!

July 10, 2014
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$Mathematical functions on non-numbers?!$

A question came up about regarding Q1.4.9 in Cherney, Denton, Waldron. Here is a snippet of the original question: Consider …Continue reading →

## Buster – a new R package for bagging hierarchical clustering

July 9, 2014
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$Buster – a new R package for bagging hierarchical clustering$

I recently found myself a bit stuck. I needed to cluster some data. The distances between the data points were not representable in Euclidean space so I had to use hierarchical clustering. But then I wanted stable clusters that would retain their shape as I updated the data set with new observations. This I could … Continue reading...

## recycling accept-reject rejections (#2)

July 1, 2014
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$recycling accept-reject rejections (#2)$

Following yesterday’s post on Rao’s, Liu’s, and Dunson’s paper on a new approach to intractable normalising constants, and taking advantage of being in Warwick, I tested the method on a toy model, namely the posterior associated with n Student’s t observations with unknown location parameter μ and a flat prior, which is “naturally” bounded by

## Tailoring univariate probability distributions

June 26, 2014
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This post shows how to build a custom univariate distribution in R from scratch, so that you end up with the essential functions: a probability density function, cumulative distribution function, quantile function and random number generator. In the beginning all you need is an equation of the probability density function, … Continue reading →

## A Simple Shiny App for Monitoring Trading Strategies

June 25, 2014
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In a previous post I showed how to use  R, Knitr and LaTeX to build a template strategy report. This post goes a step further by making  the analysis  interactive. Besides the interactivity, the Shiny App also solves two problems : I can now access all my trading strategies from a single point regardless of the instrument traded.

## Conditional Distributions from some Elliptical Vectors

June 18, 2014
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$\boldsymbol{X}=(\boldsymbol{X}_1,\boldsymbol{X}_2)$

This winter, in my ACT8595 course, I asked my students (that was some homework) to prove that it was possible to derive the conditional distribution when we have a Student-t random vector (and to get the analytical expression of the later). But before, let us recall a standard result about the Gaussian vector. If  is a Gaussian random vector, i.e. then  has a...

## R Markdown v2

June 18, 2014
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People rarely agree on a best authoring tool or language. Some people cannot live without \LaTeX{} because of the beauty and quality of its PDF output. Some \feel{} \uncomfortable{} \with{} \backslashes{}, and would rather live in another World Word. We have also witnessed the popularity of Markdown, an incredibly simple language (seriously? a LANGUAGE?) that has made reproducible research much easier.

## Tukey and Mosteller’s Bulging Rule (and Ladder of Powers)

June 16, 2014
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$Y_i=\beta_0+\beta_1 X_i+\varepsilon_i$

When discussing transformations in regression models, I usually briefly introduce the Box-Cox transform (see e.g. an old post on that topic) and I also mention local regressions and nonparametric estimators (see e.g. another post). But while I was working on my ACT6420 course (on predictive modeling, which is a VEE for the SOA), I read something about a “Ladder of...

## checking for finite variance of importance samplers

June 10, 2014
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Over a welcomed curry yesterday night in Edinburgh I read this 2008 paper by Koopman, Shephard and Creal, testing the assumptions behind importance sampling, which purpose is to check on-line for (in)finite variance in an importance sampler, based on the empirical distribution of the importance weights. To this goal, the authors use the upper tail