Consider a (stationary) autoregressive process, say of order 2, for some white noise with variance . Here is a code to generate such a process, __ phi1=.5 __ phi2=-.4 __ sigma=1.5 __ set.seed(1) __ n=240 __ WN=rnorm(n,sd=sigma) __ ... [Read more...]

The purpose of software engineering research is to figure out how software development works so that the software industry can improve its quality/timeliness (i.e., lower costs and improved customer satisfaction). Research is hampered by the fact that companies are not usually willing to make public good quality data ... [Read more...]

Now that every team has played a match it will be interesting to see how this has affected the (inverse) odds of victory. Since the plot in my last post was a bit ‘busy’, I have decided to use the facet_wrap function in gglplot2 to stratify by group. Also, ... [Read more...]

I finally got around to publishing my time series cross-validation package to github, and I plan to push it out to CRAN  shortly. You can clone the repo using github for mac, for windows, or linux, and then run the following script to... [Read more...]

Quantifying the success of government policies is clearly important. Randomized control trials, like those conducted by drug companies, are often described as the ‘gold-standard’ for policy evaluation. Under these, a policy is implemented in/to one area/group (treatment), but not in/to another (control). The difference in outcomes between ... [Read more...]

Has Ireland gotten any warmer? Ask any punter on the street and they will happily inform you of wild swings, trends and dips. “Back when I was a child”, “when I was younger”, or “years ago” are the usual refrains. What’s the evidence? To answer this, I will use ... [Read more...]

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In the revision of Bayesian Core on which Jean-Michel Marin and I worked together most of last week, having missed our CIRM break last summer (!), we have now included an illustration of what happens to an AR(p) time series when the customary stationarity+causality condition on the roots of ... [Read more...]

Rob Hyndman has a great post on his blog with example on how to cross-validate a time series model.  The basic concept is simple:  You start with a minimum number of observations (k), and fit a model (e.g. an arima model) to those observation... [Read more...]

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In a previous post I introduced the permute package and the function shuffle(). In that post I got as far as replicating R’s base function sample(). Here I’ll briefly outline how shuffle() can be used to generate restricted permutations. shuffle() … Continue reading → [Read more...]

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Something I have been doing a lot of work with recently are time series data, to which I have been fitting additive models to describe trends and other features of the data. When modelling temporally dependent data, we often need … Continue reading → [Read more...]

Some months ago, I posted a function that extended the base R function embed() to allow for time delay embedding. Today, David Gonzales alerted me to an inconsistency between embed() and Embed(). The example David used was where Embed() clearly … Continue reading → [Read more...]

Earlier, I looked at the HadCRUT3vgl data set using generalized least squares to investigate whether the trend in temperature since 1995 was statistically significant. Here I want to follow-up one of the points from the earlier posting; namely using a … Continue reading → [Read more...]

Yesterday (June 11, 2011) the BBC reported on comments by Prof. Phil Jones, of the Climatic Research Unit (CRU), University of East Anglia (UEA), that the warming experienced by the planet since 1995 was statistically significant. That the trend in … Continue reading → [Read more...]

(This post got published on The Statistics Forum yesterday.) The short book review section of the International Statistical Review sent me Raquel Prado’s and Mike West’s book, Time Series (Modeling, Computation, and Inference) to review. The current post is not about this specific book, but rather on why ... [Read more...]

The housing bubble by city. Miami sailed high and fell far. Detroit rose modestly and but dropped more than it went up. Dallas held steady. DC is enjoying a bit of renewed growth, but are in and New York yet to fall? [Read more...]

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