6 papers · 1 filter
Graph-dependent shrinkage priors for Bayesian trend filtering
Andrea Mascaretti, Daniel R. Kowal
Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), images appear as pixels (lattice graph), areal data are defined by neig…
Efficient Bayesian inference for two-stage models in environmental epidemiology
Konstantin Larin, Daniel R. Kowal
Statistical models often require inputs that are not completely known. This can occur when inputs are measured with error, indirectly, or when they are predicted using another mode…
A dynamic copula model for probabilistic forecasting of non-Gaussian multivariate time series
John Zito, Daniel R. Kowal
Multivariate time series (MTS) data often include a heterogeneous mix of non-Gaussian distributional features (asymmetry, multimodality, heavy tails) and data types (continuous and…
Bayesian Functional Graphical Models with Change-Point Detection
Chunshan Liu, Daniel R. Kowal, James Doss-Gollin +1
Functional data analysis, which models data as realizations of random functions over a continuum, has emerged as a useful tool for time series data. Often, the goal is to infer the…
The projected dynamic linear model for time series on the sphere
John Zito, Daniel Kowal
Time series on the unit n-sphere arise in directional statistics, compositional data analysis, and many scientific fields. There are few models for such data, and the ones that exi…
Monte Carlo inference for semiparametric Bayesian regression
Daniel R. Kowal, Bohan Wu
Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model paramete…