5 papers
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…
High-dimensional copula variational approximation through transformation
Michael Stanley Smith, Ruben Loaiza-Maya, David J. Nott
Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a targe…
Time Series Copulas for Heteroskedastic Data
Rubén Loaiza-Maya, Michael S. Smith, Worapree Maneesoonthorn
We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We develop our copula for first order Markov series, and extend it to higher…