We adopt a semiparametric conditional distribution regression model that allows inference on any functionals of counterfactual distributions, including PDFs and multiple QTEs.
We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021).
Analyzing massive spatial datasets using Gaussian process model poses computational challenges.
We propose a technique that simultaneously estimates the entire conditional distribution and flexibly allows for machine learning techniques to be incorporated.
Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes.
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.
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.
Statistical analysis of DTI data is challenging because the data are positive definite matrices.
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting.
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.
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.