1539 search results for "regression"

Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

September 12, 2012
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Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

Previously This book and the associated R package were introduced before. Executive Summary A very nice — and enlightening — discussion of a wide range of topics. Principles The Introduction to the book sets out 5 principles.  This is probably the most important part of the book.  The principles are: We don’t know much in … Continue reading...

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Population health management with RevoScaleR

September 10, 2012
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This guest post is by Douglas McNair MD PhD, Engineering Fellow & President, Cerner Math Inc. -- ed. RevoScaleR scaling big-data modeling performance for real-time health data analysis at Cerner The size of data sets is increasing much more rapidly than the speed of cores, of RAM, and of disk drives. This is particularly true of electronic health records...

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Simulation metamodeling with GNU R

September 7, 2012
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Simulation metamodeling with GNU R

I am one of the organizers of ESSA2013 conference that will take place in September 2013 in Warsaw, Poland. The conference scope is social simulation and in particular methods of statistical analysis of simulation output (metamodeling). As we have just issued Call for Papers for the conference so I decided to post a simple example of a metamodel.Recently I had...

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That damn R-squared !

September 7, 2012
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That damn R-squared !

Another post about the R-squared coefficient, and about why, after some years teaching econometrics, I still hate when students ask questions about it. Usually, it starts with "I have a _____ R-squared... isn't it too low ?" Please, feel free to fi...

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Inference and autoregressive processes

September 6, 2012
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Inference and autoregressive processes

Consider a (stationary) autoregressive process, say of order 2, for some white noise with variance . Here is a code to generate such a process, > phi1=.5 > phi2=-.4 > sigma=1.5 > set.seed(1) > n=240 > WN=rnorm(n,sd=sigma) > ...

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Visually weighted/ Watercolor Plots, new variants: Please vote!

September 6, 2012
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Visually weighted/ Watercolor Plots, new variants: Please vote!

Update Oct-23: Added a new parameter add to the function. Now multiple groups can be plotted in a single plot (see example in my comment) As a follow-up on my R implementation of Solomon’s watercolor plots, I made some improvements to the function. I fine-tuned the graphical parameters (the median smoother line now diminishes faster

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Near-instant high quality graphs in R

September 5, 2012
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Near-instant high quality graphs in R

One of the main attractions of R (for me) is the ability to produce high quality graphics that look just the way you want them to. The basic plot functions are generally excellent for exploratory work and for getting to know your data. Most packages have additional functions for appropriate exploratory work or for summarizing

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Amazing fMRI plots for everybody!

September 5, 2012
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Amazing fMRI plots for everybody!

Dear valued customer, it is a well-known scientific truth that research results which are accompanied by a fancy, colorful fMRI scan, are perceived as more believable and more persuasive than simple bar graphs or text results (McCabe & Castel, 2007; Weisberg, Keil, Goodstein, Rawson, & Gray, 2008). Readers even agree more with fictitious and unsubstantiated

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BMR: Bayesian Macroeconometrics in R

September 4, 2012
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BMR: Bayesian Macroeconometrics in R

The recently released BMR package, short for Bayesian Macroeconometrics with R, provides a comprehensive set of powerful routines that estimate Bayesian Vector Autoregression (VAR) and Dynamic Stochastic General Equilibrium (DSGE) models in R. The procedure of estimating both Bayesian VAR and DSGE models can represent a great computational burden. However, BMR removes a lot of

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Descriptive statistics of some Agile feature characteristics

September 2, 2012
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Descriptive statistics of some Agile feature characteristics

The purpose of software engineering research is to figure out how software development works so that the software industry can improve its quality/timeliness (i.e., lower costs and improved customer satisfaction). Research is hampered by the fact that companies are not usually willing to make public good quality data about the details of their software development

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