In linear regression problems with many predictors, penalized regression techniques are
often used to guard against overfitting and to select variables relevant for predicting an …

Throughout the last two decades, Bayesian statistical methods have proliferated throughout
ecology and evolution. Numerous previous references established both philosophical and …

Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian
framework, with the aim to automatically reduce time-varying parameters to static ones, if the …

Vector autoregressive (VAR) models are frequently used for forecasting and impulse
response analysis. For both applications, shrinkage priors can help improving inference. In …

We address the curse of dimensionality in dynamic covariance estimation by modeling the
underlying co-volatility dynamics of a time series vector through latent time-varying …

Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the
effect of explanatory variables on the outcome variable. However, in particular when the …

Matrix square roots and their inverses arise frequently in machine learning, eg, when
sampling from high-dimensional Gaussians N (0, K) or “whitening” a vector b against …

Raman spectroscopy is a promising tool for neurosurgical guidance and cancer research.
Quantitative analysis of the Raman signal from living tissues is, however, limited. Their …

Rapid advancements in high-throughput biological techniques have facilitated the
generation of high-dimensional omics datasets, which have provided a solid foundation for …

We propose a new variational approximation of the joint posterior distribution of the log-
volatility in the context of large Bayesian VARs. In contrast to existing approaches that are …