Monthly Archives: May 2013

Japan – JGB Yields–More Lattice Charts

May 15, 2013
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Japan – JGB Yields–More Lattice Charts

This blog is littered with posts about Japan. In one sentence, I think Japan presents opportunity and is a very interesting real-time test of much of my macro thinking. Proper visualization is absolutely essential for me to understand all of the dynami...

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Big News! “Practical Data Science with R” MEAP launched!

May 15, 2013
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Big News! “Practical Data Science with R” MEAP launched!

Nina Zumel and I ( John Mount ) have been working very hard on producing an exciting new book called “Practical Data Science with R.” The book has now entered Manning Early Access Program (MEAP) which allows you to subscribe to chapters as they become available and give us feedback before the book goes into Related posts:

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Variance matrix differences

May 15, 2013
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Variance matrix differences

Torturing portfolios to give different volatilities between a factor model and Ledoit-Wolf shrinkage. Previously There have been posts on: “What the hell is a variance matrix?” factor models Ledoit-Wolf shrinkage Question Two of the several ways to produce an estimate of the variance matrix of asset returns is a statistical factor model and Ledoit-Wolf shrinkage.  … Continue reading...

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Forecasting annual totals from monthly data

May 15, 2013
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Forecasting annual totals from monthly data

This question was posed on crossvalidated.com: I have a monthly time series (for 2009–2012 non-stationary, with seasonality). I can use ARIMA (or ETS) to obtain point and interval forecasts for each month of 2013, but I am interested in forecasting the total for the whole year, including prediction intervals. Is there an easy way in R to obtain interval...

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Easier confidence interval estimation with matrices and similar arrays in R

May 15, 2013
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When dealing with survey data in particular, social scientists are often wanting to produce proportions from the data, and associated confidence intervals. The prop.test command in R can be used to generate the desired results. When dealing with small ...

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From a random generator to a sample function

May 14, 2013
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From a random generator to a sample function

This week-end, I wrote a post since I had some trouble to generate a sample random sample with R, to reproduce one obtained by a co-author, with SAS (generated using Fishman and Moore (1982) used in function RANUNI). I was lucky since another contributor for that book, Christrophe Dutang, got the anwer to the last question I asked: is it...

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Top 3 R resources for beginners

May 14, 2013
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The community team at Revolution Analytics has just updated this list of resources to learn about R on the Web. Included is this list of the top 3 resources for absolute beginners getting started with R: An Introduction to R – The free, “official” CRAN R Manual Try R – a short course that lets you jump right in...

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Le Monde puzzle [#820]

May 14, 2013
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Le Monde puzzle [#820]

The current puzzle is… puzzling: Given the set {1,…,N} with N<61, one iterates the following procedure: take (x,y) within the set and replace the pair with the smallest divider of x+y (bar 1). What are the values of N such that the final value in the set is 61? I find it puzzling because the

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1.5 percent of doctors, a quarter of malpratice reports

May 14, 2013
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1.5 percent of doctors, a quarter of malpratice reports

Some doctors receive more malpractice reports than others. Just how unequal is the distribution of malpractice reports? The post 1.5 percent of doctors, a quarter of malpratice reports appeared first on Decision Science News.

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SIR Model – The Flue Season – Dynamic Programming

May 14, 2013
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SIR Model – The Flue Season – Dynamic Programming

# The SIR Model (susceptible, infected, and recovered) model is a common and useful tool in epidemiological modelling.# In this post and in future posts I hope to explore how this basic model can be enriched by including different population group...

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