61 citations · 64 across the 4 of their papers we have counts for
5 papers
Ensemble Kalman methods for high-dimensional hierarchical dynamic space-time models
Matthias Katzfuss, Jonathan R. Stroud, Christopher K. Wikle
We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combin…
A Bayesian adaptive ensemble Kalman filter for sequential state and parameter estimation
Jonathan R. Stroud, Matthias Katzfuss, Christopher K. Wikle
This paper proposes new methodology for sequential state and parameter estimation within the ensemble Kalman filter. The method is fully Bayesian and propagates the joint posterior…
Sequential Monte Carlo Smoothing with Parameter Estimation
Biao Yang, Jonathan R. Stroud, Gabriel Huerta
We propose two new Bayesian smoothing methods for general state-space models with unknown parameters. The first approach is based on the particle learning and smoothing algorithm,…
Bayesian and Maximum Likelihood Estimation for Gaussian Processes on an Incomplete Lattice
Jonathan R. Stroud, Michael L. Stein, Shaun Lysen
This paper proposes a new approach for Bayesian and maximum likelihood parameter estimation for stationary Gaussian processes observed on a large lattice with missing values. We pr…
Bayesian modeling and forecasting of 24-hour high-frequency volatility: A case study of the financial crisis
Jonathan R. Stroud, Michael S. Johannes
This paper estimates models of high frequency index futures returns using `around the clock' 5-minute returns that incorporate the following key features: multiple persistent stoch…