124 citations · 550 across the 52 of their papers we have counts for
58 papers
Modeling Dynamic Correlation Matrices with Shrinkage Priors
Daniel Andrew Coulson, David S. Matteson, Martin T. Wells
Estimating time-varying correlation matrices is challenging because existing methods may adapt slowly to structural changes, impose insufficient regularization, or produce diffuse…
Smoothing Variances Across Time: Adaptive Stochastic Volatility
Jason B. Cho, David S. Matteson
We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP…
Vector AutoRegressive Moving Average Models: A Review
Marie-Christine Düker, David S. Matteson, Ruey S. Tsay +1
Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vect…
Bayesian changepoint detection via logistic regression and the topological analysis of image series
Andrew M. Thomas, Michael Jauch, David S. Matteson
We present a Bayesian method for multivariate changepoint detection that allows for simultaneous inference on the location of a changepoint and the coefficients of a logistic regre…
Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series
Toryn L. J. Schafer, David S. Matteson
Non-stationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, e…
Non-fungible token transactions: data and challenges
Jason B. Cho, Sven Serneels, David S. Matteson
Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data a…