# 841 search results for "latex"

## Church numerals in R (or how to prove the existence of natural numbers using the lambda calculus)

September 18, 2013
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$Church numerals in R (or how to prove the existence of natural numbers using the lambda calculus)$

One area of math that I’ve always been enamored with is the proof of numbers. The simplicity of the starting …Continue reading »

## Forecasting with daily data

September 16, 2013
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I’ve had several emails recently asking how to forecast daily data in R. Unless the time series is very long, the simplest approach is to simply set the frequency attribute to 7. y <- ts(x, frequency=7) Then any of the usual time series forecasting methods should produce reasonable forecasts. For example library(forecast) fit <- ets(y) fc <- forecast(fit) plot(fc)...

## Profile Likelihood for New Jersey U.S. Senate Special Election

September 16, 2013
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As it stands right now Cory Booker has a very good chance of winning the New Jersey Special U.S. Senate election on October 16 to replace Frank Lautenberg and fill the remainder of his term for the next 15 months.  So with the election only about a month away I took advantage of some of

## Monty Hall (oh no, not again)

September 13, 2013
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$A$

Quite frequently, someone on the internet discovers the Monty Hall paradox, and become so enthusiastic that it becomes urgent to publish an article – or a post – about it. The latest example can be http://www.bbc.co.uk/news/magazine-24045598. I won’t blame them, I did the same a few years ago (see http://freakonometrics.hypotheses.org/776, or http://freakonometrics.hypotheses.org/775, in French). My point today is that the...

## Non-observable vs. observable heterogeneity factor

September 11, 2013
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$X$

This morning, in the ACT2040 class (on non-life insurance), we’ve discussed the difference between observable and non-observable heterogeneity in ratemaking (from an economic perspective). To illustrate that point (we will spend more time, later on, discussing observable and non-observable risk factors), we looked at the following simple example. Let  denote the height of a person. Consider the following dataset >...

## The Beta Prior, Likelihood, and Posterior

September 4, 2013
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The Beta distribution (and more generally the Dirichlet) are probably my favorite distributions.  However, sometimes only limited information is available when trying set up the distribution.  For example maybe you only know the lowest likely value, the highest likely value and the median, as a measure of center.  That information is sufficient to construct a

## Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
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Getting Started with Structural Equation Modeling Part 1Getting Started with Structural Equation Modeling: Part 1 Introduction For the analyst familiar with linear regression fitting structural equation models can at first feel strange. In the R environment, fitting structural equation models involves learning new modeling syntax, new plotting...

## Introducing ‘propagate’

August 31, 2013
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$Introducing ‘propagate’$

With this post, I want to introduce the new ‘propagate’ package on CRAN. It has one single purpose: propagation of uncertainties (“error propagation”). There is already one package on CRAN available for this task, named ‘metRology’ (http://cran.r-project.org/web/packages/metRology/index.html). ‘propagate’ has some additional functionality that some may find useful. The most important functions are: * propagate: A

## Encouraging citation of software – introducing CITATION files

August 30, 2013
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Summary: Put a plaintext file named CITATION in the root directory of your code, and put information in it about how to cite your software. Go on, do it now – it’ll only take two minutes! Software is very important in science – but good software takes time and effort that could be used to do