1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Logistic Gaussian process density regression: a generalized Bayesian approach
Zichuan Chen, Lucas Kock, Jeong Eun Lee +1
Density regression extends conventional parametric regression by allowing the entire distribution of the response to vary flexibly with covariates rather than just low-order moment…
Calibrated Multivariate Regression with Localized PIT Mappings
Lucas Kock, G. S. Rodrigues, Scott A. Sisson +2
Calibration ensures that predicted uncertainties align with observed uncertainties. While there is an extensive literature on recalibration methods for univariate probabilistic for…
Model-Free Local Recalibration of Neural Networks
R. Torres, D. J. Nott, S. A. Sisson +3
Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been m…
Regression Copulas for Multivariate Responses
Nadja Klein, Michael Stanley Smith, David Nott +1
We propose a novel distributional regression model for a multivariate response vector based on a copula process over the covariate space. It uses the implicit copula of a Gaussian…