1539 search results for "regression"

Data is everywhere!

November 19, 2011
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Data is everywhere!

I was writing earlier today that I am getting really fed to using the same datasets over and over again. Of course using the same data over time with different methods (eg look this) serves really well on a comparison scope but still we can use other data in a web world. For example, you

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Style Analysis

November 17, 2011
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Style Analysis

During the final stage of asset allocation process we have to decide how to implement our desired allocation. In many cases we will allocate capital to the mutual fund managers who will invest money according to their fund’s mandate. Usually there is no perfect relationship between asset classes and fund managers. To determine the true

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Power-laws: choose your x and y variables carefully

November 16, 2011
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Power-laws: choose your x and y variables carefully

This is a follow-up of the post Power of running world records As suggested by Andrew, plotting running world records could benefit from a change of variables. More exactly the use of different variables sheds light on a well-known sports result provided in a 2000 Nature paper by Sandra Savaglio and Vincenzo

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Announcing Revolution R Enterprise 5.0

November 15, 2011
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We're proud to announce the latest update to the enhanced, commercial-grade distribution of R, Revolution R Enterprise 5.0. With each new release, Revolution R Enterprise adds more capabilities to open-source R, to make R users more productive, to improve performance of R programs, to support Big Data analytics, and to provide servers and APIs for enterprise deployment. New features...

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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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