Perspectives from the INFORMS 2017 Annual Meeting

Tahir Ekin, McCoy College of Business, Texas State University. The INFORMS Annual Meeting was held in Houston, TX on October 22-25th, 2017. Initially, there were concerns about the readiness of Houston to host the conference after Hurricane Harvey. The organizing committee conducted a series of evaluations and decided to help the city get back to … Continue reading Perspectives from the INFORMS 2017 Annual Meeting

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Sharing WISDOM at the Women in Statistics and Data Science Conference

Kimberly F. Sellers, Department of Mathematics and Statistics, Georgetown University The 2017 Women in Statistics and Data Science (WSDS) conference occurred on October 19-21, 2017 in La Jolla, California, bringing together women statisticians from industry, academia, and government. WSDS is a unique, three-day conference that features plenary talks from leaders in their respective fields, as … Continue reading Sharing WISDOM at the Women in Statistics and Data Science Conference

“To p or not to p” –my thoughts on the ASA Symposium on Statistical Inference.

Ron S. Kenett, KPA Group, Samuel Neaman Institute, Technion and Institute for Drug Research, Hebrew University, Jerusalem, Israel. (ron@kpa-group.com). An eminent statistician labeled the American Statistical Association (ASA) statement on p values with the title of this blog. This post is about the ASA SSI held in Bethesda Maryland on October 11-12th, 2017, and my own … Continue reading “To p or not to p” –my thoughts on the ASA Symposium on Statistical Inference.

Using statistics and data science to build a crowdsourcing data platform

by Ankur Gupta, Machine Learning Scientist, Premise Data, San Francisco.   Increased internet connectivity has allowed large numbers of people to work towards a single goal in a distributed fashion. This practice is called crowdsourcing and we see successful examples of crowdsourcing everywhere. The most famous example is perhaps Wikipedia, which allows anyone in the world … Continue reading Using statistics and data science to build a crowdsourcing data platform

Highlighting Interesting Articles that are NOT in the Statistics Literature

David Steinberg.  Most of us come across new ideas and interesting research by attending conferences and reading journals. Naturally, we begin with those meetings and journals that are in our own field. However, many articles with interesting statistical content appear in other journals and meetings. This should not be surprising: statistics is a part of … Continue reading Highlighting Interesting Articles that are NOT in the Statistics Literature

50 Years of Data Science

By David Steinberg.  Data Science has become a rallying cry for universities, research organizations, and many commercial and industrial companies. We are surrounded by ever increasing amounts of data and by myriad methods and algorithms to take advantage of them. Rallying cry aside, no one seems to be very clear about just what IS data … Continue reading 50 Years of Data Science

Wallenius Naïve Bayes

David Steinberg. One of the simplest methods for two-group classification is naïve Bayes, in which predictors are treated as though they provide independent information. Traditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behavior. This paper develops a new event model, based on a … Continue reading Wallenius Naïve Bayes

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

David Steinberg. This paper addresses a classical problem in causal inference: matching, where treatment units need to be matched to control units. Some of the main challenges in developing matching methods arise from the tension among (i) inclusion of as many covariates as possible in defining the matched groups, (ii) having matched groups with enough … Continue reading FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining

David Steinberg. Yang et al. consider the application of predictive data mining techniques in Information Systems research. Their focus is on the impact of data errors and misclassification on the subsequent data analysis by econometric models. Typically, data mining methods are first used to generate new variables (e.g., text sentiment), which are added into subsequent … Continue reading Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining

The Surprising Power of Online Experiments

David Steinberg. One of the hot topics in internet commerce is A/B testing – the use of designed experiments to maximize revenue from web sites. The fact that experimental design is a great way to test ideas should not be a surprise to readers of this column. And many businesses have caught on to the … Continue reading The Surprising Power of Online Experiments