Earlier this month, Inbal Yahav (Bar Ilan University) and I presented our joint work on detecting Simpson's Paradox in big data as a poster at ECIS 2014 (thanks to the many interested visitors!), and at 2014 SCECR. This work describes an unusual use of classification and regression trees for a causal goal, rather than their normal use in prediction. We develop a tree variant that helps detect possible paradoxes in large datasets. The research-in-progress paper is available here, and the longer version is available on SSRN.
"The Forest or the Trees? Tackling Simpson's Paradox in Big Data with Trees" - at ECIS 2014
"Too Big To Fail" — invited talk at Israel Statistical Association annual conference
I'll talk about the problem of statistical inference with large samples in the closing panel with the killer title "Too much data + too much statistics = too many errors?" of the annual conference of the Israel Statistical Association. The session takes place on June 11, 2014 at 16:45 at the Open University in Raanana.
Indian editions now available on amazon.in, flipkart.com
Online course "Forecasting Analytics" opens this Friday
I'll be instructing a 4-week online course Forecasting Analytics at Statistics.com. Learn about popular forecasting methods and how to implement them in practice through a hands-on online course. This is a practical course that introduces forecasting methods, performance evaluation, and best practices. The course is of interest to practitioners and researchers in business, environmental sciences, agriculture, healthcare, tourism and any field that collects time series data. It is also useful for instructors developing a new forecasting analytics course. The text for the course is Practical Time Series Forecasting: A Hands-On Guide (2nd edition), available globally as softcover (US edition, Indian edition, Chinese edition) and Kindle edition.
"Predictive Analytics for Business Growth" now available online
My article "Predictive Analytics for Business Growth" which appeared in the ISBInsight magazine (volume 10 issue 21) is now available online. "In this article, Professor Galit Shmuéli explores the salient characteristics of predictive analytics in the context of retail, debunks certain myths and suggests untapped regionally-specific new possibilities."