1104 search results for "LaTeX"

Power and Sample Size Analysis: Z test

October 17, 2012
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Power and Sample Size Analysis: Z test

This article provide a brief background about power and sample size analysis. Then, power and sample size analysis is computed for the Z test. Continue reading →

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Fractals and Kronecker product

October 17, 2012
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Fractals and Kronecker product

A few years ago, I went to listen to Roger Nelsen who was giving a talk about copulas with fractal support. Roger is amazing when he gives a talk (I am also a huge fan of his books, and articles), and I really wanted to play with that concept ...

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Ready-made model comparison tables for journals

October 15, 2012
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Ready-made model comparison tables for journals

If you're reporting on the results of a statistical analysis for a journal or report, you'll probably be building a table comparing two or models. Such tables may include variables in the model, parameter estimates, and p-values, and model summary statistics. If you want to include such tables based on lm, glm, svyglm, gee, gam, polr, survreg or coxph...

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Compound Poisson and vectorized computations

October 12, 2012
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Compound Poisson and vectorized computations

Yesterday, I was asked how to write a code to generate a compound Poisson variables, i.e. a series of random variables  where  is a counting random variable (here Poisson disributed) and where the 's are i.i.d (and ind...

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Working with Bipartite/Affiliation Network Data in R

September 30, 2012
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Working with Bipartite/Affiliation Network Data in R

Data can often be usefully conceptualized in terms affiliations between people (or other key data entities). It might be useful analyze common group membership, common purchasing decisions, or common patterns of behavior. This post introduces bipartite/affiliation network data and provides … Continue reading →

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

September 27, 2012
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Using R in Political Controversies: Unemployment Reduction Prowess Under Bush versus Obama Years

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.

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Bounding sums of random variables, part 1

September 27, 2012
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Bounding sums of random variables, part 1

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...

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Learning Kernels SVM

September 25, 2012
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Learning Kernels SVM

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 →

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Maximum likelihood estimates for multivariate distributions

September 22, 2012
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Maximum likelihood estimates for multivariate distributions

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 ...

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Maximum likelihood estimates for multivariate distributions

September 22, 2012
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Maximum likelihood estimates for multivariate distributions

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) >...

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