Monthly Archives: January 2014

Inference for AR(p) Time Series

January 28, 2014
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Inference for AR(p) Time Series

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...

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Lies, Damn Lies, “Data Journalism” and Charts That Don’t Start at 0

January 28, 2014
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Lies, Damn Lies, “Data Journalism” and Charts That Don’t Start at 0

This tweet by @moorehn (who usually is a superb economic journalist) really bugged me: Alarming chart of employment for people between 25 and 54. It's like a ski jump. #SOTUecon pic.twitter.com/KNGYmwI88C— Heidi N. Moore (@moorehn) January 29, 2014 I grabbed the raw data from EPI: (http://www.epi.org/files/2012/data-swa/jobs-data/Employment%20to%20population%20ratio%20(EPOPs).xls) and properly started the graph at 0 for the

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cut, baby, cut!

January 28, 2014
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cut, baby, cut!

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

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Time series data in R

January 28, 2014
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There is no shortage of time series data available on the web for use in student projects, or self-learning, or to test out new forecasting algorithms. It is now relatively easy to access these data sets directly in R. M Competition data The 1001 series from the M-competition and the 3003 series from the M3-competition are available as part...

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Binomial testing with buttered toast

January 28, 2014
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Rasmus' post of last week on binomial testing made me think about p-values and testing again. In my head I was tossing coins, thinking about gender diversity and toast. The toast and tossing a buttered toast in particular was the most helpful thought experiment, as I didn't have a fixed opinion on the probabilities for a toast to...

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Analyzing Sleep with Sleep Cycle App and R

January 28, 2014
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Analyzing Sleep with Sleep Cycle App and R

I have been tracking my sleep for almost two years now using my Fitbit. I started with the Fitbit Ultra and then moved on the the Fitbit One after it came out. In October 2013 I found out about the Sleep Cycle (Link) app for the iPhone. For weeks, Sleep Cycle was listed as the … Continue reading...

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Ryan Peek on Creating Shiny Apps

January 28, 2014
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Yesterday at the Davis R User’s Group1, Ryan Peek gave a talk about using the shiny package to create interactive web apps with R. Here are his slides. Ryan includes a bunch of links to examples and tutorials, as well as his own thermohydrographs app: Thanks to Revolution Analytics for another year of...

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Context Matters When Modeling Human Judgment and Choice

January 28, 2014
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Context Matters When Modeling Human Judgment and Choice

Herbert Simon was succinct when he argued that judgment and choice "is shaped by a scissor whose two blades are the structure of the task environment and the computational capabilities of the actor" (Simon, 1990, p.7). As a marketing researcher, I take...

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Finding out repeated variables in multiple datasets

January 28, 2014
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Finding out repeated variables in multiple datasets

Few days ago I posted on doing a smart job on importing several data files alike from a directory. Today, I want to return to this topic, but stretching it a bit further by adding some complexity. I want to have a snapshot of the datasets even before starting work with them. That is, I

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Bias of Hill Estimators

January 28, 2014
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Bias of Hill Estimators

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...

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