# 1290 search results for "LaTeX"

## Reserving with negative increments in triangles

April 11, 2013
By
$Y_i$

A few months ago, I did published a post on negative values in triangles, and how to deal with them, when using a Poisson regression (the post was published in French). The idea was to use a translation technique: Fit a model not on ‘s but on , for some , Use that model to make predictions, and then...

## High Obesity levels found among fat-tailed distributions

April 11, 2013
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In my never ending quest to find the perfect measure of tail fatness, I ran across this recent paper by Cooke, Nieboer, and Misiewicz. They created a measure called the “Obesity index.” Here’s how it works: Step 1: Sample four times from a distribution. The sample points should be independent and identically distributed (did your

## Dirichlet Process, Infinite Mixture Models, and Clustering

April 7, 2013
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The Dirichlet process provides a very interesting approach to understand group assignments and models for clustering effects.   Often time we encounter the k-means approach.  However, it is necessary to have a fixed number of clusters.  Often we encounter situations where we don’t know how many fixed clusters we need.  Suppose we’re trying to identify

## Subtraction Is Crazy

April 4, 2013
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$\large \dpi{200} \bg_white f ^\prime \equiv \lim_{\mathrm{lag} \downarrow 0} {\mathrm{lag} (f)-f \over |\mathrm{lag}| }$

I was re-reading Michael Murray’s explanation of cointegration: and marvelling at the calculus. Calculus blows my mind sometimes. Like, hey guess how much we can do with subtraction. — protëa(@isomorphisms) March 28, 2013 Of course it’s not any subtraction. It’s subtracting a function from a shifted version of itself. Still doesn’t sound like a universal revolution. (But of course the...

## Tables Are Like Cockroaches

April 3, 2013
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As much as I would like to completely replace all tables with beautiful, intuitive, and interactive charts, tables like cockroaches cannot be eliminated. Based on this very interesting discussion on the Perceptual Edge forum with source Exploring the Origins of Tables for Information Visualization, tables date back to 1850 BCE. The paper concludes with As part of...

## R 3.0.0 is released! (what’s new, and how to upgrade)

April 3, 2013
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A few hours ago Peter Dalgaard (of R Core Team) announced the release of R 3.0.0!  Bellow you can read the changes in this release. One of the features worth noticing is the introduction of long vectors to R 3.0.0. As David Smith …Read more »

## CFP: the 11th Australasian Data Mining Conference (AusDM 2013), submission due 15 July

April 3, 2013
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********************************************************************* The 11th Australasian Data Mining Conference (AusDM 2013) Canberra, Australia, 13-15 November 2013, http://ausdm13.togaware.com Join us on LinkedIn: http://www.linkedin.com/groups/AusDM-4907891 ********************************************************************* Data mining, the art and science of intelligent analysis of (usually large) data sets for meaningful (and previously unknown) … Continue reading →

## p-values are (possibly biased) estimates of the probability that the null hypothesis is true

March 31, 2013
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$p-values are (possibly biased) estimates of the probability that the null hypothesis is true$

Last week, I posted about statisticians’ constant battle against the belief that the p-value associated (for example) with a regression coefficient is equal to the probability that the null hypothesis is true, for a null hypothesis that beta is zero or negative. I argued that (despite our long pedagogical practice) there are, in fact, many

## How do Dew and Fog Form? Nature at Work with Temperature, Vapour Pressure, and Partial Pressure

In the early morning, especially here in Canada, I often see dew – water droplets formed by the condensation of water vapour on outside surfaces, like windows, car roofs, and leaves of trees.  I also sometimes see fog – water droplets or ice crystals that are suspended in air and often blocking visibility at great

## Checking for Normality with Quantile Ranges and the Standard Deviation

$Checking for Normality with Quantile Ranges and the Standard Deviation$

Introduction I was reading Michael Trosset’s “An Introduction to Statistical Inference and Its Applications with R”, and I learned a basic but interesting fact about the normal distribution’s interquartile range and standard deviation that I had not learned before.  This turns out to be a good way to check for normality in a data set.