964 search results for "latex"

Seasonal, or periodic, time series

March 20, 2014
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Seasonal, or periodic, time series

Monday, in our MAT8181 class, we’ve discussed seasonal unit roots from a practical perspective (the theory will be briefly mentioned in a few weeks, once we’ve seen multivariate models). Consider some time series , for instance traffic on French roads, > autoroute=read.table( + "http://freakonometrics.blog.free.fr/public/data/autoroute.csv", + header=TRUE,sep=";") > X=autoroute$a100 > T=1:length(X) > plot(T,X,type="l",xlim=c(0,120)) > reg=lm(X~T) > abline(reg,col="red") As discussed in a...

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Fast computation of cross-validation in linear models

March 17, 2014
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Fast computation of cross-validation in linear models

The leave-one-out cross-validation statistic is given by     where , are the observations, and is the predicted value obtained when the model is estimated with the th case deleted. This is also sometimes known as the PRESS (Prediction Residual Sum of Squares) statistic. It turns out that for linear models, we do not actually have to estimate the...

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Moving the North Pole to the Equator

March 15, 2014
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Moving the North Pole to the Equator

I am still working with @3wen on visualizations of the North Pole. So far, it was not that difficult to generate maps, but we started to have problems with the ice region in the Arctic. More precisely, it was complicated to compute the area of this region (even if we can easily get a shapefile). Consider the globe, worldmap <- ggplot()...

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Testing for trend in ARIMA models

March 12, 2014
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Testing for trend in ARIMA models

Today’s email brought this one: I was wondering if I could get your opinion on a particular problem that I have run into during the reviewing process of an article. Basically, I have an analysis where I am looking at a couple of time-series and I wanted to know if, over time there was an upward trend in the...

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where did the normalising constants go?! [part 2]

March 11, 2014
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where did the normalising constants go?! [part 2]

Coming (swiftly and smoothly) back home after this wonderful and intense week in Banff, I hugged my loved ones,  quickly unpacked, ran a washing machine, and  then sat down to check where and how my reasoning was wrong. To start with, I experimented with a toy example in R: and (of course!) it produced the

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where did the normalising constants go?! [part 1]

March 10, 2014
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where did the normalising constants go?! [part 1]

When listening this week to several talks in Banff handling large datasets or complex likelihoods by parallelisation, splitting the posterior as and handling each term of this product on a separate processor or thread as proportional to a probability density, then producing simulations from the mi‘s and attempting at deriving simulations from the original product,

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How effective is my research programming workflow? The Philip Test – Part 1

March 10, 2014
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How effective is my research programming workflow? The Philip Test – Part 1

Philip Guo, who writes a wonderful blog on his views and experiences of academia – including a lot of interesting programming stuff – came up with a research programming version of The Joel Test last summer, and since then I’ve been thinking of writing a series commenting on how well I fulfil each of the items on

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Displaying time series, spatial, and space-time data with R is available for pre-order

Displaying time series, spatial, and space-time data with R is available for pre-order

Two years ago, motivated by a proposal from John Kimmel, Executive Editor at Chapman & Hall/CRC Press, I started working …Sigue leyendo →

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Writing a book with a little help from Emacs and friends

Writing a book with a little help from Emacs and friends

This post provides technical details about the making of my book “Displaying time series, spatial, and space-time data with R”, …Sigue leyendo →

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Near-zero variance predictors. Should we remove them?

March 6, 2014
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Near-zero variance predictors. Should we remove them?

Datasets come sometimes with predictors that take an unique value across samples. Such uninformative predictor is more common than you might think. This kind of predictor is not only non-informative, it can break some models you may want to fit to your data (see example below). Even more common is the presence of predictors that

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