A Hybrid NUTS-Gibbs Sampler with State Space Marginalization for Estimation of Dynamic Structural Equation Models with Binomial Outcomes
This paper presents a hybrid sampler – alternating between one step of the No-U-Turn Sampler (NUTS) and one Gibbs step – for estimating dynamic structural equation models with binomial outcomes. The Gibbs step handles Pólya-Gamma distributed latent variables arising from a logit link, and the NUTS step uses a Kalman filter to marginalize over latent states. We demonstrate that the proposed sampler makes DSEM estimation with binomial data feasible for larger data and models than previously possible. arXiv preprint.