A reposted item of news about MCMSki IV: as posted by Brad Carlin this afternoon to the Biometrics Section and Bayesian Statistical Science Section of the ASA, The fifth joint international meeting of the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), nicknamed “MCMSki IV”, will ... [Read more...]

by Joseph Rickert At Revolution Analytics our mission is to establish R as the driver for Enterprise level computational frameworks. In part, this means that a data scientist ought to be able to develop an R based application in one context, e.g. her local PC, and then get it ... [Read more...]

Data analysis is mostly focused on structured and standardized data, e. g. data from data bases, because these data can be used easily for analysis. Nevertheless even unstructured data offer chances to generate advantages. Concrete applications like content analysis or sentiment detection are discussed more and more frequently. Of course, ... [Read more...]

A short talk that I gave at the LIGHTS 2013 Conference (Johannesburg, 12 September 2013). The slides are relatively devoid of text because I like the audience to hear the content rather than read it. The central message of the presentation is that clustering lightning discharges into storms is not a trivial task, ... [Read more...]

Ensuring the quality and correctness of statistical or scientific software in general constitute as one for the main responsibilities of scientific software developers and scientists who provide a code to solve a specific computational task. Sometimes tasks could be mission critical. For example, in drug trails, clinical research or designing ... [Read more...]

Ensuring the quality and correctness of statistical or scientific software in general constitute as one fo the main responsibilities of scientific software developers and scientists who provide a code to solve a specific computational task. Sometimes t... [Read more...]

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I found a fun survey released to the Toronto Open Data website that investigates the travel/commuting behaviour of Torontonians, but with a special focus on E-bikes.  When I opened up the file, I found various demographic information, in addition to a … Continue reading → [Read more...]

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I often find it beneficial to check to see whether or not a dataset is already loaded into R at the beginning of a file. This is particularly helpful when I'm dealing with a large file that I don't want to load repeatedly, and when I might be using the... [Read more...]

I thought a good extension of the last post d3-ify Systematic Investor Cluster Weight would be to analyze the returns with every table that PerformanceAnalytics offers.  Since d3 interactive charts are way more fun than tables, let’s plot each table with dimplejs and rCharts. Click here or on ... [Read more...]

During the 2013 JSM (Joint Statistics Meetings) Conference in Montreal, Revolution Analytics conducted a survey of attendees from August 5 to August 8. The 865 respondents gave their opinions on the privacy and ethics related to data collection, and on their familiarity with statistical software used for the analysis of such data. Out of ... [Read more...]

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 ... [Read more...]

The Problem with Percentiles Percentiles (or, more accurately, quantiles) are deeply embedded in the psyche of actuaries, statisticians and similar beasts. They are referred to implicitly in the Solvency 2 directive (Article 100, Value at Risk) without explanation. They are so ingrained that we normally use them without thinking, which I was ... [Read more...]

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This morning, in the ACT2040 class (on non-life insurance), we’ve discussed the difference between observable and non-observable heterogeneity in ratemaking (from an economic perspective). To illustrate that point (we will spend more time, later on, discussing observable and non-observable risk factors), we looked at the following simple example. Let  ... [Read more...]

This was a presentation made to few of my colleagues. The idea was to provide a brief introduction to R, provide few reasons for my switching to it, and give some samples of graphics/interactive visualizations that could be created with minimal coding.... [Read more...]

[This article was first published on Rmetrics blogs, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your [Read more...]

It's a "given" that your empirical results should be able to be replicated by others. That's why more and more journals are encouraging or requiring that authors of such papers "deposit" their data and code with the journal as a condition of acceptance for publication. That's all well and good. ... [Read more...]