6 papers · 1 filter
Implicit Copulas: An Overview
Michael Stanley Smith
Implicit copulas are the most common copula choice for modeling dependence in high dimensions. This broad class of copulas is introduced and surveyed, including elliptical copulas,…
Fast and Accurate Variational Inference for Models with Many Latent Variables
Rubén Loaiza-Maya, Michael Stanley Smith, David J. Nott +1
Models with a large number of latent variables are often used to fully utilize the information in big or complex data. However, they can be difficult to estimate using standard app…
Marginally-calibrated deep distributional regression
Nadja Klein, David J. Nott, Michael Stanley Smith
Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on ac…
Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach
Nadja Klein, Michael Stanley Smith
We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the joint dis…
Bayesian Inference for Regression Copulas
Michael Stanley Smith, Nadja Klein
We propose a new semi-parametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula i…
Implicit Copulas from Bayesian Regularized Regression Smoothers
Nadja Klein, Michael Stanley Smith
We show how to extract the implicit copula of a response vector from a Bayesian regularized regression smoother with Gaussian disturbances. The copula can be used to compare smooth…