Monthly Archives: February 2013

An infelicity with Value at Risk

February 4, 2013
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An infelicity with Value at Risk

More risk does not necessarily mean bigger Value at Risk. Previously “The incoherence of risk coherence” suggested that the failure of Value at Risk (VaR) to be coherent is of little practical importance. Here we look at an attribute that is not a part of the definition of coherence yet is a desirable quality. Thought … Continue reading...

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analyze the survey of income and program participation (sipp) with r

February 4, 2013
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if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp).  it's giant.  it's rich with variables.  it's monthly.  it follows households over three, four, now five year panels.  the congressional budget office uses it for their health insurance simulation.  analysts read that sipp has...

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Proposed techniques for communicating the amount of information contained in a statistical result

February 4, 2013
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Proposed techniques for communicating the amount of information contained in a statistical result

A couple of weeks ago, I posted about how much we can expect to learn about the state of the world on the basis of a statistical significance test. One way of framing this question is: if we’re trying to come to scientific conclusions on the basis of statistical results, how much can we update

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Data Visualization for Education

February 3, 2013
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Recently I was invited to give a talk to two cohorts of Strategic Data Project fellows. I was asked to speak about using data visualization to help inform decision-making of policy makers. At the same time, the group had a lot of variation in their int...

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A Grid Search for The Optimal Setting in Feed-Forward Neural Networks

February 3, 2013
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A Grid Search for The Optimal Setting in Feed-Forward Neural Networks

The feed-forward neural network is a very powerful classification model in the machine learning content. Since the goodness-of-fit of a neural network is majorly dominated by the model complexity, it is very tempting for a modeler to over-parameterize the neural network by using too many hidden layers or/and hidden units. As pointed out by Brian

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Japanese Government Bonds (JGB) Total Return Series

February 3, 2013
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Japanese Government Bonds (JGB) Total Return Series

In a follow up to Yen and JGBs Short-Term vs Long Term and a series of posts on Japan, I thought the Bloomberg article "Japan Pension Fund’s Bonds Too Many If Abe Succeeds, Mitani Says" was particularly interesting.  It is difficult to find a to...

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An Example of Seasonality Analysis

February 3, 2013
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An Example of Seasonality Analysis

Today, I want to demonstrate how easy it is to create a seasonality analysis study and produce a sample summary report. As an example study, I will use S&P Annual Performance After a Big January post by Avondale Asset Management. The first step is to load historical prices and find Big Januaries. All the hard

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The Rcpp Gallery and my <em>Seinfeld Streak</em>

February 3, 2013
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A good three weeks ago, we introduced the Rcpp Gallery. While this is a joint effort by several of us on the Rcpp team, the backend was conceived and implemented entirely by JJ who also bootstrapped it with same first content, drawing on posts by Ha...

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The Rcpp Gallery and my Seinfeld Streak

February 3, 2013
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A good three weeks ago, we introduced the Rcpp Gallery. While this is a joint effort by several of us on the Rcpp team, the backend was conceived and implemented entirely by JJ who also bootstrapped it with same first content, drawing on posts by Ha...

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Checking validation statistics (Monitor function 030220139)

February 3, 2013
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(This article was first published on NIR-Quimiometria, and kindly contributed to R-bloggers) To leave a comment for the author, please follow the link and comment on his blog: NIR-Quimiometria. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics...

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