Mar 2026
A Hybrid NUTS-Gibbs Sampler with State Space Marginalization for Estimation of Dynamic Structural Equation Models with Binomial Outcomes
This paper presents a hybrid NUTS-Gibbs sampler for dynamic structural equation models with binomial outcomes. The Gibbs step handles Pólya-Gamma latent variables from a logit link, while the NUTS step uses a Kalman filter to marginalize over latent states. arXiv preprint.
Mar 2026
Efficient Bayesian Estimation of Dynamic Structural Equation Models via State Space Marginalization
This paper shows that the within-level part of any dynamic structural equation model can be reformulated as a linear Gaussian state space model, enabling analytical marginalization via a Kalman filter and highly efficient estimation via Hamiltonian Monte Carlo. arXiv preprint.
Jan 2025
Modeling Cycles, Trends and Time-Varying Effects in Dynamic Structural Equation Models with Regression Splines
This paper proposes regression splines for flexibly modeling nonlinear trends, cycles, and time-varying effects in dynamic structural equation models. Published in Multivariate Behavioral Research.