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Applications: Choice Models, Data Fusion, Dyadic Data, Marketing Data, Textual Analysis Methodologies: Bayesian Methods, Machine Learning, Nonparametrics

Fred Feinberg

Professor, Business>Marketing, Statistics

My research examines how people make choices in uncertain environments. The general focus is on using statistical models to explain complex decision patterns, particularly involving sequential choices among related items (e.g., brands in the same category) and dyads (e.g., people choosing one another in online dating), as well as a variety of applications to problems in the marketing domain (e.g., models relating advertising exposures to awareness and sales). The main methods used lie primarily in discrete choice models, ordinarily estimated using Bayesian methods, dynamic programming, and nonparametrics. I’m particularly interested in extending Bayesian analysis to very large databases, especially in terms of ‘fusing’ data sets with only partly overlapping covariates to enable strong statistical identification of models across them.

Applying Bayesian Methods to Problems in Dynamic Choice

Applying Bayesian Methods to Problems in Dynamic Choice