1536 search results for "Regression"

How do I Create the Identity Matrix in R?

June 27, 2012
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How do I Create the Identity Matrix in R?

I googled for this once upon a time and nothing came up. Hopefully this saves someone ten minutes of digging about in the documentation. You make identity matrices with the keyword diag, and the number of dimensions in parentheses. > diag(3) [,...

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Factor Attribution 2

June 26, 2012
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Factor Attribution 2

I want to continue with Factor Attribution theme that I presented in the Factor Attribution post. I have re-organized the code logic into the following 4 functions: factor.rolling.regression – Factor Attribution over given rolling window factor.rolling.regression.detail.plot – detail time-series plot and histogram for each factor factor.rolling.regression.style.plot – historical style plot for selected 2 factors factor.rolling.regression.bt.plot

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Crazy RUT in Academic Context Why Trend is Not Your Friend

June 26, 2012
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Crazy RUT in Academic Context Why Trend is Not Your Friend

In response to Where are the Fat Tails?, reader vonjd very helpfully referred me to this paper The Trend is Not Your Friend! Why Empirical Timing Success is Determined by the Underlying’s Price Characteristics and Market Efficiency is Irrelevant by P...

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useR 2012: main conference braindump

June 24, 2012
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useR 2012: main conference braindump

I knew R was versatile, but DANG, people do a lot with it: > > … I don’t think anyone actually believes that R is designed to make *everyone* happy. For me, R does about 99% of the things I … Continue reading →

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Optimal sorting using rpart

June 24, 2012
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Optimal sorting using rpart

Some time ago I read a nice post Solving easy problems the hard way where linear regression is used to solve an interesting puzzle. Following the idea I used rpart to find optimal decision tree sorting five elements.It is well known that...

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Actuarial models with R, Meielisalp

June 23, 2012
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Actuarial models with R, Meielisalp

I will be giving a short course in Switzerland next week, at the 6th R/Rmetrics Meielisalp Workshop & Summer School on Computational Finance and Financial Engineering organized by ETH Zürich, https://www.rmetrics.org/. The long...

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Factor Attribution

June 19, 2012
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Factor Attribution

I came across a very descriptive visualization of the Factor Attribution that I will replicate today. There is the Three Factor Rolling Regression Viewer at the mas financial tools web site that performs rolling window Factor Analysis of the “three-factor model” of Fama and French. The factor returns are available from the Kenneth R French:

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Reproducible reports & research with knitr in R Studio

June 18, 2012
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Reproducible reports & research with knitr in R Studio

Arguably, knitr (CRAN link) is the most outstanding R package of this year and its creator, Yihui Xie is the star of the useR! conference 2012. This is because the ease of use comparing to Sweave for making reproducible report. Integration of knitR and R Studio has made reproducible research much more convenience, intuitive and easier to

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Standard, Robust, and Clustered Standard Errors Computed in R

June 15, 2012
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Standard, Robust, and Clustered Standard Errors Computed in R

Where do these come from? Since most statistical packages calculate these estimates automatically, it is not unreasonable to think that many researchers using applied econometrics are unfamiliar with the exact details of their computation. For the purposes of illustration, I am going to estimate different standard errors from a basic linear regression model: , using the

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Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks.

June 13, 2012
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Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks.

AbstractVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods—multiple linear regression and artificial neural networks—that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods,...

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