Estimation and Inference for Assortment Optimization We present two works on assortment optimization. In the first part, we consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model … Read more
Every Statistical Problem is a Missing Data/Causal Inference Problem Across numerous fields of domain science research, challenges arise routinely where data of interest are missing and/or inferences are required regarding, for example, the causal effects of treatments based on observational … Read more
Existence of MLEs for the parameters of an adjusted linear preferential attachment model of random graphs with covariates In this talk, we consider a variation on the preferential attachment model (PAM) of random graphs with covariates. It is similar to … Read more
Short Biography:David Aldous is Professor Emeritus in the Statistics Dept at U.C. Berkeley. He is the author of "Probability Approximations via the Poisson Clumping Heuristic" and (with Jim Fill) of a notorious unfinished online work "Reversible Markov Chains and Random Walks … Read more
STATISTICS AND OPERATIONS RESEARCH NEW STUDENT ORIENTATION Friday August 18, 2023 Room 120, Hanes Hall 10:00am - 12:00pm
Professor Amir Dembo, Dept. of Statistics, Stanford University The Hotelling Lectures are an annual event in the Department of Statistics & Operations Research at the University of North Carolina Chapel Hill, honoring the memory of Professor Harold Hotelling, our first … Read more
Professor Amir Dembo, Dept. of Statistics, Stanford University The Hotelling Lectures are an annual event in the Department of Statistics & Operations Research at the University of North Carolina Chapel Hill, honoring the memory of Professor Harold Hotelling, our first … Read more
Analysis of Covariance: Model-based and Randomization-based For randomized clinical trials with at least moderate sample size, adjustment of comparisons between treatments for baseline covariables can be helpful for two reasons. One is enhancement of power, and the other is … Read more
Bayesian Forensic DNA Mixture Deconvolution With a Novel String Similarity Measure Abstract: Forensic scientists have great interest in deconvolving profiles in crime scene samples, particularly to test the presence of a courtroom suspect. However, DNA degradation, low material quantity, … Read more
Neural Collapse in Deep Net Training Modern deep neural networks are structurally complex and massively over-parameterized. One might then expect these networks to exhibit many particularities with little regularity across architectures and applications. However, through extensive experiments on contest winning … Read more
Energetic Variational Inference In this talk, I plan to highlight one component of my on-going research, energetic variational inference. Variational Inference (VI) is an important research area in the field of machine learning. Many VI approaches have been developed and … Read more
Statistical Tools for Inference on Samples of Networks with Applications to Neuroimaging Networks are an increasingly common data structure, and their growing popularity demands the development of new statistical methodology. In this talk I'll discuss several projects motivated by applications … Read more
