1495 search results for "Regression"

Conference in Lyon on climate change and insurance

November 14, 2011
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Conference in Lyon on climate change and insurance

I will be in Lyon next Monday to give a talk on "Modeling heat-waves: return period for non-stationary extremes" in a workshop entitled "Changement climatique et gestion des risques". An interesting reference might be some pages from Le Monde (201...

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Another look at autocorrelation in the S&P 500

November 11, 2011
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Another look at autocorrelation in the S&P 500

Casting doubt on the possibility of mean reversion in the S&P 500 lately. Previously A look at volatility estimates in “The mystery of volatility estimates from daily versus monthly returns” led to considering the possibility of autocorrelation in the returns.  I estimated an AR(1) model through time and added a naive confidence interval to the … Continue reading...

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The mystery of volatility estimates from daily versus monthly returns

November 8, 2011
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The mystery of volatility estimates from daily versus monthly returns

What drives the estimates apart? Previously A post by Investment Performance Guy prompted “Variability of volatility estimates from daily data”. In my comments to the original post I suggested that using daily data to estimate volatility would be equivalent to using monthly data except with less variability.  Dave, the Investment Performance Guy, proposed the exquisitely … Continue reading...

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Bayesian modeling using WinBUGS

November 6, 2011
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Bayesian modeling using WinBUGS

Yes, yet another Bayesian textbook: Ioannis Ntzoufras’ Bayesian modeling using WinBUGS was published in 2009 and it got an honourable mention at the 2009 PROSE Award. (Nice acronym for a book award! All the mathematics books awarded that year were actually statistics books.) Bayesian modeling using WinBUGS is rather similar to the more recent Bayesian

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Unit root versus breaking trend: Perron’s criticism

November 4, 2011
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Unit root versus breaking trend: Perron’s criticism

I came across an ingenious simulation by Perron during my Time-series lecture which I thought was worth sharing. The idea was to put your model to a further test of breaking trend before accepting the null of unit root. Let me try and illustrate this in simple language. A non-stationary time series is one that has its mean changing...

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Confidence interval for predictions with GLMs

November 4, 2011
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Confidence interval for predictions with GLMs

Consider a (simple) Poisson regression . Given a sample where , the goal is to derive a 95% confidence interval for given , where is the prediction. Hence, we want to derive a confidence interval for the prediction, not the potential observation...

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Confidence interval for predictions with GLMs

November 4, 2011
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Confidence interval for predictions with GLMs

Consider a (simple) Poisson regression . Given a sample where , the goal is to derive a 95% confidence interval for given , where is the prediction. Hence, we want to derive a confidence interval for the prediction, not the potential observation, i.e. the dot on the graph below > r=glm(dist~speed,data=cars,family=poisson) > P=predict(r,type="response", + newdata=data.frame(speed=seq(-1,35,by=.2))) > plot(cars,xlim=c(0,31),ylim=c(0,170)) > abline(v=30,lty=2)...

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How much does "Beta" change depending on time?

November 1, 2011
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How much does "Beta" change depending on time?

You may often use "Beta" to measure the market exposure of your portfolio because it's easy to calculate.Since I have been wondering how much "Beta" change depending on time, more precisely writing, data-set and the period of return time series, I...

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Bootstrapping a Single Statistic (k=1) The following example…

November 1, 2011
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Bootstrapping a Single Statistic (k=1)

The following example…

Bootstrapping a Single Statistic (k=1) The following example generates the bootstrapped 95% confidence interval for R-squared in the linear regression of miles per gallon (mpg) on car weight (wt) and displacement (disp). The data source is mtcars. The...

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Using Sparse Matrices in R

October 31, 2011
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Using Sparse Matrices in R

Introduction I’ve recently been working with a couple of large, extremely sparse data sets in R. This has pushed me to spend some time trying to master the CRAN packages that support sparse matrices. This post describes three of them: the Matrix, slam and glmnet packages. The first two packages provide data storage classes for

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