Publications

2011
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

2013
Book

Keeping Up with the Quants: Your Guide to Understanding and Using Analytics

By: Thomas H. Davenport and Jinho Kim

Managers today need to be able to analyze and make sense of data. They need to be conversant with analytical technology and methods and to make decisions on quantitative analysis. This book offers a variety of practical tools and examples to improve a manager's... View Details

Keywords: Statistics; Business Analytics; Big Data; Analytics and Data Science; Management Practices and Processes; Mathematical Methods; Information Management

2015
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

10 Dec 2014
News

Yes, A/B Testing Is Still Necessary

Keywords: statistics; experimentation; testing; products

Article

Fast Generalized Subset Scan for Anomalous Pattern Detection

By: Edward McFowland III, Skyler Speakman and Daniel B. Neill

We propose Fast Generalized Subset Scan (FGSS), a new method for detecting anomalous patterns in general categorical data sets. We frame the pattern detection problem as a search over subsets of data records and attributes, maximizing a nonparametric scan statistic... View Details

Keywords: Pattern Detection; Anomaly Detection; Knowledge Discovery; Bayesian Networks; Scan Statistics; Analytics and Data Science

Article

Fast Subset Scan for Multivariate Spatial Biosurveillance

By: Daniel B. Neill, Edward McFowland III and Huanian Zheng

We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details

Keywords: Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan

March 2022 (Revised January 2025)
Technical Note

Exploratory Data Analysis

By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton

This module note provides an overview of exploratory data analysis for an introduction to data science course. It begins by defining the term "data", and then describes the different types of data that companies work with (structured v. unstructured, categorical v.... View Details

Keywords: Data Analysis; Data Science; Statistics; Data Visualization; Exploratory Data Analysis; Analytics and Data Science; Analysis

Educators

2024
Working Paper

Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python

By: Melissa Ouellet and Michael W. Toffel

This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details

Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods

SSRN

2016
Article

Does volunteering improve well-being?

By: A.V. Whillans, Scott C. Seider, Lihan Chen, Ryan J. Dwyer, Sarah Novick, Kathryn J. Gramigna, Brittany A. Mitchell, Victoria Savalei, Sally S. Dickerson and Elizabeth W. Dunn

Does volunteering causally improve well-being? To empirically test this question, we examined one instantiation of volunteering that is common at post-secondary institutions across North America: community service learning (CSL). CSL is a form of experiential learning... View Details

Keywords: Prosocial Behavior; College Students; Bayesian Statistics; Education; Well-being

March 2020
Article

Is This My Group or Not? The Role of Ensemble Coding of Emotional Expressions in Group Categorization

By: Amit Goldenberg, Timothy D. Sweeny, Emmanuel Shpigel and James J. Gross

When exposed to others’ emotional responses, people often make rapid decisions as to whether these others are members of their group or not. These group categorization decisions have been shown to be extremely important to understanding group behavior. Yet, despite... View Details

Keywords: Categorization; Ensemble Coding; Summary Statistical Perception; Social Cognition; Emotions; Perception; Groups and Teams

August 2021
Article

Multiple Imputation Using Gaussian Copulas

By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky

Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details

Keywords: Missing Data; Bayesian Statistics; Imputation; Categorical Data; Estimation

2022
Article

Nonparametric Subset Scanning for Detection of Heteroscedasticity

By: Charles R. Doss and Edward McFowland III

We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning... View Details

Keywords: Scan Statistics; Anomaly Detection; Regression; Model Diagnostics

August 2020 (Revised September 2020)
Technical Note

Assessing Prediction Accuracy of Machine Learning Models

By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne

The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details

Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods

Educators

July–August 2024
Article

Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals

By: Ta-Wei Huang and Eva Ascarza

Firms are increasingly interested in developing targeted interventions for customers with the best response, which requires identifying differences in customer sensitivity, typically through the conditional average treatment effect (CATE) estimation. In theory, to... View Details

Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy

Article

Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error

By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details

Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science