Search Results for author: Brian J. Reich

Found 12 papers, 5 papers with code

A Bayesian Semiparametric Method For Estimating Causal Quantile Effects

no code implementations3 Nov 2022 Steven G. Xu, Shu Yang, Brian J. Reich

We adopt a semiparametric conditional distribution regression model that allows inference on any functionals of counterfactual distributions, including PDFs and multiple QTEs.

Causal Inference

SPQR: An R Package for Semi-Parametric Density and Quantile Regression

no code implementations26 Oct 2022 Steven G. Xu, Reetam Majumder, Brian J. Reich

We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021).

regression

Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods

1 code implementation24 Dec 2020 Suman Majumder, Yawen Guan, Brian J. Reich, Arvind K. Saibaba

Analyzing massive spatial datasets using Gaussian process model poses computational challenges.

Methodology Computation

Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting

no code implementations17 Aug 2020 David B. Huberman, Brian J. Reich, Howard D. Bondell

We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated.

BIG-bench Machine Learning Decision Making +1

DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction

1 code implementation23 Jul 2020 Wanfang Chen, Yuxiao Li, Brian J. Reich, Ying Sun

Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes.

Gaussian Processes General Classification

Selecting Diverse Models for Scientific Insight

no code implementations16 Jun 2020 Laura J. Wendelberger, Brian J. Reich, Alyson G. Wilson

Model selection often aims to choose a single model, assuming that the form of the model is correct.

Model Selection regression +1

Global forensic geolocation with deep neural networks

no code implementations28 May 2019 Neal S. Grantham, Brian J. Reich, Eric B. Laber, Krishna Pacifici, Robert R. Dunn, Noah Fierer, Matthew Gebert, Julia S. Allwood, Seth A. Faith

An important problem in forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing.

Nearest-Neighbor Neural Networks for Geostatistics

no code implementations28 Mar 2019 Haoyu Wang, Yawen Guan, Brian J. Reich

We propose a more flexible spatial prediction method based on the Nearest-Neighbor Neural Network (4N) process that embeds deep learning into a geostatistical model.

A Spatial Bayesian Semiparametric Mixture Model for Positive Definite Matrices with Applications to Diffusion Tensor Imaging

1 code implementation18 Mar 2019 Zhou Lan, Brian J. Reich, Dipankar Bandyopadhyay

Statistical analysis of DTI data is challenging because the data are positive definite matrices.

Methodology Applications

Deep Distribution Regression

1 code implementation14 Mar 2019 Rui Li, Howard D. Bondell, Brian J. Reich

Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting.

Decision Making General Classification +2

Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior

1 code implementation31 Aug 2016 Yan Dora Zhang, Brian P. Naughton, Howard D. Bondell, Brian J. Reich

The proposed method compares favourably to previous approaches in terms of both concentration around the origin and tail behavior, which leads to improved performance both in posterior contraction and in empirical performance.

Methodology

A spatial capture-recapture model for territorial species

no code implementations8 May 2014 Brian J. Reich, Beth Gardner

Advances in field techniques have lead to an increase in spatially-referenced capture-recapture data to estimate a species' population size as well as other demographic parameters and patterns of space usage.

Applications

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