# 911 search results for "latex"

## Census Open Atlas Project Version Two

February 5, 2014
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This time last year I published the first version of the 2011 Census Open Atlas which comprised Output Area Level census maps for each local authority district. This turned out to be quite a popular project, and I have also extended this to Japan.The methods used to construct the atlases have now been refined,...

## Caching API calls offline

February 2, 2014
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I've recently heard the idea of "offline first" via especially Hood.ie. We of course don't do web development, but primarily build R interfaces to data on the web. Internet availablility is increasinghly ubiqutous, but there still are times and places where you don't have internet, but need to get work done.In the R packages we write there...

## Inference for ARMA(p,q) Time Series

January 30, 2014
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$ARMA(1,1)$

As we mentioned in our previous post, as soon as we have a moving average part, inference becomes more complicated. Again, to illustrate, we do not need a two general model. Consider, here, some  process, where  is some white noise, and assume further that . > theta=.7 > phi=.5 > n=1000 > Z=rep(0,n) > set.seed(1) > e=rnorm(n) > for(t...

## Inference for MA(q) Time Series

January 29, 2014
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$AR(p)$

Yesterday, we’ve seen how inference for time series was possible.  I started  with that one because it is actually the simple case. For instance, we can use ordinary least squares. There might be some possible bias (see e.g. White (1961)), but asymptotically, estimators are fine (consistent, with asymptotic normality). But when the noise is (auto)correlated, then it is more...

## Inference for AR(p) Time Series

January 28, 2014
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$Y_t =\varphi_1 Y_{t-1}+\varphi_2 Y_{t-2}+\varepsilon_t$

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=.25 > phi2=.7 > n=1000 > set.seed(1) > e=rnorm(n) > Z=rep(0,n) > for(t in 3:n) Z=phi1*Z+phi2*Z+e > Z=Z > n=length(Z) > plot(Z,type="l") Here, we have to estimate two sets of parameters: the autoregressive...

## cut, baby, cut!

January 28, 2014
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At MCMSki IV, I attended (and chaired) a session where Martyn Plummer presented some developments on cut models. As I was not sure I had gotten the idea [although this happened to be one of those few sessions where

## Bias of Hill Estimators

January 28, 2014
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$\overline{F}(x)=C x^{-\alpha}$

In the MAT8595 course, we’ve seen yesterday Hill estimator of the tail index. To be more specific, we did see see that if , with , then Hill estimators for are given by for . Then we did say that satisfies some consistency in the sense that if , but not too fast, i.e. (under additional assumptions on the...

## New in forecast 5.0

January 26, 2014
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Last week, version 5.0 of the forecast package for R was released. There are a few new functions and changes made to the package, which is why I increased the version number to 5.0. Thanks to Earo Wang for helping with this new version. Handling missing values and outliers Data cleaning is often the first step that data scientists...

## Thoughts on the Ljung-Box test

January 23, 2014
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It is common to use a Ljung-Box test to check that the residuals from a time series model resemble white noise. However, there is very little practical advice around about how to choose the number of lags for the test. The Ljung-Box test was proposed by Ljung and Box (Biometrika, 1978) and is based on the statistic    ...

## Plain Text, Papers, Pandoc

January 23, 2014
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Over the past few months, I've had several people ask me about the tools I use to put papers together. For several year's I've maintained a page of resources somewhat grandiosely headed "Writing and Presenting Social Science". Really it just makes public my configuration files and templates for my text editor and related tools. Things have changed a...