no code implementations • 16 Apr 2024 • Elliot Maceda, Emily C. Hector, Amanda Lenzi, Brian J. Reich
In this paper, we propose a framework for Bayesian posterior estimation by mapping data to posteriors of parameters using a neural network trained on data simulated from the complex model.
no code implementations • 3 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.
no code implementations • 26 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).
1 code implementation • 24 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
no code implementations • 17 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.
1 code implementation • 23 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.
no code implementations • 16 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.
no code implementations • 28 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.
no code implementations • 28 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.
1 code implementation • 18 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
1 code implementation • 14 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.
1 code implementation • 31 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
no code implementations • 8 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