# 1290 search results for "LaTex"

## Using R in Political Controversies: Unemployment Reduction Prowess Under Bush versus Obama Years

September 27, 2012
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Editor’s note: R-bloggers does not take a political side. Since this is an important topic, this post has the comments turned on. Also, If you wish to write a reply post (which includes an R context), you are welcome to contact me to have it published. This post was written by Prof. H. D. Vinod. Fordham University, New York.

## Bounding sums of random variables, part 1

September 27, 2012
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$\Delta$

For the last course MAT8886 of this (long) winter session, on copulas (and extremes), we will discuss risk aggregation. The course will be mainly on the problem of bounding  the distribution (or some risk measure, say the Value-at-Risk) for two random variables with given marginal distribution. For instance, we have two Gaussian risks. What could be be worst-case scenario...

## Learning Kernels SVM

September 25, 2012
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Machine Learning and Kernels A common application of machine learning (ML) is the learning and classification of a set of raw data features by a ML algorithm or technique. In this context a ML kernel acts to the ML algorithm … Continue reading →

## Maximum likelihood estimates for multivariate distributions

September 22, 2012
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Consider our loss-ALAE dataset, and - as in Frees & Valdez (1998) - let us fit a parametric model, in order to price a reinsurance treaty. The dataset is the following, > library(evd) > data(lossalae) > Z=lossalae > X=Z;Y=Z ...

## Maximum likelihood estimates for multivariate distributions

September 22, 2012
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Consider our loss-ALAE dataset, and – as in Frees & Valdez (1998) - let us fit a parametric model, in order to price a reinsurance treaty. The dataset is the following, > library(evd) > data(lossalae) > Z=lossalae > X=Z;Y=Z The first step can be to estimate marginal distributions, independently. Here, we consider lognormal distributions for both components, > Fempx=function(x) mean(X<=x) >...

## Power Analysis and the Probability of Errors

September 22, 2012
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$Power Analysis and the Probability of Errors$

Power analysis is a very useful tool to estimate the statistical power from a study. It effectively allows a researcher to determine the needed sample size in order to obtained the required statistical power. Clients often ask (and rightfully so) what the sample size should be for a proposed project. Sample sizes end up being

## (nonparametric) Copula density estimation

September 20, 2012
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Today, we will go further on the inference of copula functions. Some codes (and references) can be found on a previous post, on nonparametric estimators of copula densities (among other related things).  Consider (as before) the loss-ALAE data...

## (nonparametric) copula density estimation

September 19, 2012
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Today, we will go further on the inference of copula functions. Some codes (and references) can be found on a previous post, on nonparametric estimators of copula densities (among other related things).  Consider (as before) the loss-ALAE dataset (since we’ve been working a lot on that dataset) > library(MASS) > library(evd) > X=lossalae > U=cbind(rank(X)/(nrow(X)+1),rank(X)/(nrow(X)+1)) The standard tool to plot...

## Copulas and tail dependence, part 3

September 18, 2012
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We have seen extreme value copulas in the section where we did consider general families of copulas. In the bivariate case, an extreme value can be writtenwhere is Pickands dependence function, which is a convex function satisfyingObserve that in ...

## Copulas and tail dependence, part 2

September 18, 2012
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An alternative to describe tail dependence can be found in the Ledford & Tawn (1996) for instance. The intuition behind can be found in Fischer & Klein (2007)). Assume that and have the same distribution. Now, if we assume that those vari...