Monthly Archives: January 2013

Open Science Challenge

January 8, 2013
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Open Science Challenge

Open Science Science is becoming more open in many areas: publishing, data sharing, lab notebooks, and software. There are many benefits to open science. For example, sharing research data alongside your publications leads to increased citation ra...

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Handling Strings with Rcpp

January 8, 2013
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This is a quick example of how you might use Rcpp to send and receive R ‘strings’ to and from R. We’ll demonstrate this with a few operations. Sort a String with R Note that we can do this in R in a fairly fast way: my_strings <-...

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Using Rcout for output synchronised with R

January 8, 2013
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The Writing R Extensions manual, which provides the gold standard of documentation as far as extending R goes, suggests to use Rprintf and REprintf for output (from C/C++ code) as these are matched to the usual output and error streams maintained by R ...

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Open Science Challenge

January 8, 2013
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Open Science Challenge

Open Science Science is becoming more open in many areas: publishing, data sharing, lab notebooks, and software. There are many benefits to open science. For example, sharing research data alongside your publications leads to increased citation ra...

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Reserving based on log-incremental payments in R, part I

January 8, 2013
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A recent post on the PirateGrunt blog on claims reserving inspired me to look into the paper Regression models based on log-incremental payments by Stavros Christofides , published as part of the Claims Reserving Manual (Version 2) of the Institute of Actuaries.The paper is available together with a spread sheet model, illustrating the calculations. It...

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The myth of the missing Data Scientist

January 7, 2013
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The myth of the missing Data Scientist

Much has been said about the dire shortage of Data Scientists looming on the horizon. With the spectre of Big …Continue reading »

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Does anything NOT beat the GARCH(1,1)?

January 7, 2013
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Does anything NOT beat the GARCH(1,1)?

In their paper on GARCH model comparison, Hansen and Lunde (2005) present evidence that among 330 different models, and using daily data on the DM/$ rate and IBM stock returns, no model does significantly better at predicting volatility (based on a realized measure) than the GARCH(1,1) model, for an out of sample period of about

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Does anything NOT beat the GARCH(1,1)?

January 7, 2013
By
Does anything NOT beat the GARCH(1,1)?

In their paper on GARCH model comparison, Hansen and Lunde (2005) present evidence that among 330 different models, and using daily data on the DM/$ rate and IBM stock returns, no model does significantly better at predicting volatility (based on a realized measure) than the GARCH(1,1) model, for an out of sample period of about

Read more »

Creating a Covariance Matrix from Scratch

January 7, 2013
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I have been conducting several simulations that use a covariance matrix.  I needed to expand the code that I found in the psych package to have more than 2 latent variables (the code probably allows it but I didn’t figure it out).  I ran across Joreskog’s 1971 paper and realized that I could use the confirmatory factor analysis...

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Creating a Covariance Matrix from Scratch

January 7, 2013
By

I have been conducting several simulations that use a covariance matrix.  I needed to expand the code that I found in the psych package to have more than 2 latent variables (the code probably allows it but I didn’t figure it out).  I ran across Joreskog’s 1971 paper and realized that I could use the confirmatory factor analysis...

Read more »