Lauren A. Hannah

TL;DR: A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a \multiscoop" generalization of the beta-Bernoulli process.

TL;DR: The authors proposed Dirichlet process mixtures of generalized linear models (DP-GLM) for nonparametric regression, which allows both continuous and categorical inputs and can model the same class of responses that can be modeled with a generalized linear model.

TL;DR: This work introduces convex adaptive partitioning (CAP), which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions and demonstrates empirical performance by comparing the performance of CAP to other shape-constrained and unconstrained regression methods for predicting weekly wages and value function approximation for pricing American basket options.

TL;DR: A new method, approximate dynamic programming for storage, to solve storage problems with continuous, convex decision sets, that allows math programming to be used to make decisions each time period, even in the presence of large state variables.

TL;DR: This paper uses nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state s, and proposes two solution methods that depend on the problem characteristics: function-based and gradient-based optimization.