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  • 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
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  • 2023
  • Working Paper

What Drives Variation in Investor Portfolios? Estimating the Roles of Beliefs and Risk Preferences
By: Mark Egan, Alexander MacKay and Hanbin Yang
We present an empirical model of portfolio choice that allows for nonparametric estimation of investors’ (subjective) expectations and risk preferences. Using a comprehensive dataset of 401(k) plans from 2009 through 2019, we explore the heterogeneity in asset...  View Details
Keywords: Stock Market Expectations; Demand Estimation; Retirement Planning; Defined Contribution Retirement Plan; 401 (K); Finance; Investment Portfolio; Investment; Retirement; Behavioral Finance; Financial Services Industry; United States
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  • November 2021
  • Article

Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative...  View Details
Keywords: Panel Data; Dynamic Causal Effects; Potential Outcomes; Finite Population; Nonparametric; Mathematical Methods
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  • 2020
  • Working Paper

Detecting Routines in Ridesharing: Implications for Customer Management
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal...  View Details
Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation
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  • 2020
  • Working Paper

A General Theory of Identification
By: Iavor Bojinov and Guillaume Basse
What does it mean to say that a quantity is identifiable from the data? Statisticians seem to agree on a definition in the context of parametric statistical models — roughly, a parameter θ in a model P = {Pθ : θ ∈ Θ} is identifiable if the mapping θ 7→ Pθ is injective....  View Details
Keywords: Identification; Econometric Models; Analytics and Data Science; Theory
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  • 2019
  • Article

Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
By: Iavor I Bojinov and Neil Shephard
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal...  View Details
Keywords: Causality; Nonparametric; Potential Outcomes; Trading Costs; Mathematical Methods
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  • 2018
  • Working Paper

Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides...  View Details
Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects
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  • Winter 2016
  • Article

Analytics for an Online Retailer: Demand Forecasting and Price Optimization
By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time...  View Details
Keywords: Internet and the Web; Price; Forecasting and Prediction; Revenue; Sales; Retail Industry
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  • 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
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  • August 2006
  • Article

Confidence Intervals for Probabilities of Default
By: Samuel G. Hanson and Til Schuermann
In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation...  View Details
Keywords: Credit Risk; Bootstrap; Mathematical Methods; Credit; Risk Management
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  • 1991
  • Article

Nonparametric Estimation of the Correlation Exponent
By: E. S. Mayfield and B. Mizrach
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