PLS and Prediction: two upcoming talks

Partial Least Squares Path Modeling (PLS-PM) is a popular statistical modeling tool in information systems, marketing and other social science disciplines. Researchers using PLS-PM have typically focused on explanatory modeling. I will be discussing PLS from a predictive point of view at two upcoming conferences this month:

Seminar talk at NTHU's Institute of Statistics (joint with NCTU)

On Friday, April 17, I'll be presenting a talk on "To Explain or To Predict?" at National Tsing Hua University's Institute of Statistics. This is a joint seminar with National Chiao Tung University's Institute of Statistics.

Time: 10:40am
Location: General Building III, Room 837, Institute of Statistics, NTHU
More information and abstract

Launching data mining contest using #EZTABLE data

Our Institute of Service Science in collaboration with Taiwan's restaurant reservation website EZTABLE, is launching a data mining contest using two years of EZTABLE bookings data. The goal is to predict repeat-booking customers. This Taiwan-based open data mining contest is the first of its kind!

"Opening data with Kaggle": talk at #OpenDataHyd

I describe collaborations with India start ups to create data mining contests on platforms such as kaggle.com. A great way to open real, interesting, local data. Provides business analytics students with real problems and data as well as access to the domain experts; gives start ups novel creative ideas for using their data, visibility to the world, and relationship with academia.

"Predicting, Explaining and the Business Analytics Toolkit": Keynote at the upcoming 2014 NASSCOM Big Data & Analytics Summit

I'll be presenting a keynote talk at the upcoming NASSCOM Big Data & Analytics Summit on Friday, June 27, 2014. In earlier talks, I have been emphasizing and introducing the advantages of predictive analytics. In this talk, I start from predictive analytics and move on to causal explanation.

Synopsis:

"The Forest or the Trees? Tackling Simpson's Paradox in Big Data with Trees" - at ECIS 2014

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.

"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.

"On Information Quality": seminar at Indian Statistical Institute

I'll present a talk "On Information Quality" at the Indian Statistical Institute in Kolkata's Bayesian & Interdisciplinary Research Unit on February 19th (4:15pm at BIRU conference room, 4th floor of R.A. Fisher Bhavan).

Abstract:

Last talk for 2013: "To Explain or To Predict?" at IIM-Ahmedabad

I'll be giving a seminar talk on "To Explain or To Predict" at the Production and Quantitative Methods group at IIM-A on December 31, 2013.

Venue: Wing 11, Faculty Lounge, IIM Ahmedabad
Date: Tuesday, December 31, 2013

"To Explain or To Predict?" at Rotterdam School of Management

I'll be giving a talk on "To Explain or To Predict?" at Rotterdam School of Management on Monday, Nov 4, 2013. My host, Otto Koppius, is also my co-author on a related MISQ paper "Predictive Analytics in IS Research".

Talks