research
∙
12/14/2019
Monotone function estimation in the presence of extreme data coarsening: Analysis of preeclampsia and birth weight in urban Uganda
This paper proposes a Bayesian hierarchical model to characterize the re...
share
research
∙
11/19/2019
Large-Scale Spatiotemporal Density Smoothing with the Graph-fused Elastic Net: Application to Ride-sourcing Driver Productivity Analysis
Ride-sourcing or transportation network companies (TNCs) provide on-dema...
research
∙
05/22/2019
Targeted Smooth Bayesian Causal Forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation
This article introduces Targeted Smooth Bayesian Causal Forests, or tsbc...
research
∙
10/25/2018
Optimal post-selection inference for sparse signals: a nonparametric empirical-Bayes approach
A large body of recent Bayesian work has focused on the question of how ...
research
∙
09/27/2018
Evaluation of Ride-Sourcing Search Frictions and Driver Productivity: A Spatial Denoising Approach
This paper considers the problem of spatial and temporal mispricing of r...
research
∙
05/19/2018
Functional response regression with funBART: an analysis of patient-specific stillbirth risk
This article introduces functional BART, a new approach for functional r...
research
∙
04/01/2018
Socioeconomic bias in influenza surveillance
Individuals in low socioeconomic brackets are considered at-risk for dev...
research
∙
08/06/2017
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
We consider the problem of estimating a regression function in the commo...
research
∙
02/23/2017
GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification
We consider the problem of estimating a regression function in the commo...
research
∙
02/23/2017
Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning
We present an approach to deep estimation of discrete conditional probab...
research
∙
06/07/2016
Better Conditional Density Estimation for Neural Networks
The vast majority of the neural network literature focuses on predicting...
research
∙
05/24/2015
A Fast and Flexible Algorithm for the Graph-Fused Lasso
We propose a new algorithm for solving the graph-fused lasso (GFL), a me...
research
∙
02/24/2015
Tensor decomposition with generalized lasso penalties
We present an approach for penalized tensor decomposition (PTD) that est...
research
∙
02/11/2015
Proximal Algorithms in Statistics and Machine Learning
In this paper we develop proximal methods for statistical learning. Prox...
research
∙
04/12/2014
Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes
We define a family of probability distributions for random count matrice...
research
∙
05/31/2013
Expectation-maximization for logistic regression
We present a family of expectation-maximization (EM) algorithms for bina...
research
∙
05/02/2012
Bayesian inference for logistic models using Polya-Gamma latent variables
We propose a new data-augmentation strategy for fully Bayesian inference...
research
∙
09/11/2011