362 search results for "evaluation"

informative hypotheses (book review)

September 18, 2013
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informative hypotheses (book review)

The title of this book Informative Hypotheses somehow put me off from the start: the author, Hebert Hoijtink, seems to distinguish between informative and uninformative (deformative? disinformative?) hypotheses. Namely, something like H0: μ1=μ2=μ3=μ4 is “very informative” and the alternative Ha is completely uninformative, while the “alternative null” H1: μ1<μ2=μ3<μ4 is informative. (Hence the < signs on

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BCEA in UseR!

September 13, 2013
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BCEA in UseR!

In a recent post, I had hinted at big news for BCEA $-$ I thought it was pretty much a done deal, but because it wasn't yet set in stone, I didn't want to jinx it...But now I've sorted all the details with Springer, who have asked me to write a book on the...

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Direction of Change Forecasting using a Dynamic Binary Model

September 12, 2013
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Direction of Change Forecasting using a Dynamic Binary Model

While it is generally accepted that the returns of financial assets are almost impossible to forecast with any degree of accuracy which would provide meaningful profit1 , there is evidence that the sign of the returns is much more forecastable. Theoretically, Christoffersen and Diebold (2006) have shown how the forecastability of the sign is related

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The Happiest Emoticons

September 10, 2013
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The Happiest Emoticons

Clearly, a :) is happier than a :( but what about a :-* and a :-D ? Or a :-| and a :-o ? In this post I attempt to rank emoticons in order of how happy someone has to be to use each one. (And punctuate horribly to avoid mixing punctuation with the emot...

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Online course on forecasting using R

September 10, 2013
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I am teaming up with Revolution Analytics to teach an online course on forecasting with R. Topics to be covered include seasonality and trends, exponential smoothing, ARIMA modelling, dynamic regression and state space models, as well as forecast accuracy methods and forecast evaluation techniques such as cross-validation. I will talk about some of my consulting experiences, and explain the...

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Classification of the Hyper-Spectral and LiDAR Imagery using R (mostly). Part 2: Classification Approach and Spectre Profile Creation

August 3, 2013
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Classification of the Hyper-Spectral and LiDAR Imagery using R (mostly). Part 2: Classification Approach and Spectre Profile Creation

IntroductionThis is the second part of my post series related to hyper-spectral and LiDAR imagery using R. See other parts: Part 1: Result Evaluation. In this part I will describe my general approach to classification process and then I will show you how to create cool spectral response plots like this:

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bdvis development version available for early feedback

July 30, 2013
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bdvis development version available for early feedback

Google Summer of Code 2013 is half way through. Mid term evaluations are underway. I thought this is a good logical point for us to share what we have been doing for Biodiversity Data Visualizations in R project and open up the package for testing and some early feedback. We have named the package bdvis.

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bdvis development version available for early feedback

July 30, 2013
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bdvis development version available for early feedback

Google Summer of Code 2013 is half way through. Mid term evaluations are underway. I thought this is a good logical point for us to share what we have been doing for Biodiversity Data Visualizations in R project and open up the package for testing and some early feedback. We have named the package bdvis.

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Visual debugging with RStudio

July 22, 2013
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Visual debugging with RStudio

Introduction From release 098.208 the last RStudio IDE comes with a visual debugger. Now debugging with R and RStudio becomes a simple and efficient task. This short post does not want to be a crash course: “debugging with R” nor … Continue reading →

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Optimising a Noisy Objective Function

July 16, 2013
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Optimising a Noisy Objective Function

I am busy with a project where I need to calibrate the Heston Model to some Asian options data. The model has been implemented as a function which executes a Monte Carlo (MC) simulation. As a result, the objective function is rather noisy. There are a number of algorithms for dealing with this sort of problem, and

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