Publications by Joseph Rickert
Princeton vs. Facebook: modeling contagion
by James Paul Peruvankal, Senior Program Manager at Revolution Analytics Three weeks ago, researchers at Princeton released a study on Epidemiological modeling of online social network dynamics that states Facebook might lose 80% of its users by 2015-2017. Facebook data scientists hilariously debunked the study stating that Princeton itself would...
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Sampling from a torus
by Joseph Rickert One of the key ideas in topological data analysis is to consider a data set to be a sample from a manifold in some high dimensional topological space and then to use the tools of algebraic topology to reconstruct the manifold. It turns out that the converse problem of taking a random sample from a given topological manifold also...
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Quantitative Finance Applications in R – 4: Using the Generalized Lambda Distribution to Simulate Market Returns
by Daniel Hanson, QA Data Scientist, Revolution Analytics Introduction As most readers are well aware, market return data tends to have heavier tails than that which can be captured by a normal distribution; furthermore, skewness will not be captured either. For this reason, a four parameter distribution such as the Generalized Lambda Distributio...
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R User Groups Update
by Joseph Rickert After an end-of-year slow down in R user group acrtivity that lasted into mid January, the Revolution Analytics’ Community Calendar indicates that R user groups worldwide are back in full swing with 48 events listed in the short month of February. And, while the total number of active user groups has not grown from this time l...
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Emacs, ESS and R for Zombies
by Rodney Sparapani, PhD Rodney is an Assistant Professor in the Institute for Health and Society from the Division of Biostatistics at the Medical College of Wisconsin in Milwaukee and president of the Milwaukee Chapter of the ASA which is hosting an R workshop on Data Mining in Milwaukee on April 4th. Emacs Speaks Statistics (ESS) is a GPL sof...
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Getting Started with Hidden Markov Models in R
by Joseph Rickert In addition to the considerable benefit of being able to meet other, like-minded R users face-to-face, R user groups fill a niche in the world of R education by providing a forum for communicating technical information in an informal and engaging manner. Conferences such as useR!, JSM and countless smaller statistical meetings s...
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An R "meta" book
by Joseph Rickert I am a book person. I collect books on all sorts of subjects that interest me and consequently I have a fairly extensive collection of R books, many of which I find to be of great value. Nevertheless, when I am asked to recommend an R book to someone new to R I am usually flummoxed. R is growing at a fantastic rate, and people c...
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Secrets of Teaching R
by James Paul Peruvankal, Senior Program Manager at Revolution Analytics At Revolution Analytics, we are always interested in how people teach and learn R, and what makes R so popular, yet ‘quirky’ to learn. To get some insight from a real pro we interviewed Bob Muenchen. Bob is the author of R for SAS and SPSS Users and, with Joseph M. Hi...
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Data Sets for Data Science
by Joseph Rickert Recently, I had the opportunity to be a member of a job panel for Mathematics, Economics and Statistics students at my alma mater, CSUEB (California State University East Bay). In the context of preparing for a career in data science a student at the event asked: “Where can I find good data sets?”. This triggered a number of...
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A Thumbnail History of Ensemble Methods
By Mike Bowles Ensemble methods are the backbone of machine learning techniques. However, it can be a daunting subject for someone approaching it for the first time, so we asked Mike Bowles, machine learning expert and serial entrepreneur to provide some context. Ensemble Methods are among the most powerful and easiest to use of predictive analyt...
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