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stat.ME2023
Dirichlet process mixture models for the Analysis of Repeated Attempt Designs
Michael J. Daniels, Minji Lee, Wei Feng
In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful infor…
stat.ME2023
A Bayesian Non-parametric Approach for Causal Mediation with a Post-treatment Confounder
Woojung Bae, Michael J. Daniels, Michael G. Perri
We propose a new Bayesian non-parametric (BNP) method for estimating the causal effects of mediation in the presence of a post-treatment confounder. We specify an enriched Dirichle…
stat.CO2023
Truncation Approximation for Enriched Dirichlet Process Mixture Models
Natalie Burns, Michael J. Daniels
Enriched Dirichlet process mixture (EDPM) models are Bayesian nonparametric models which can be used for nonparametric regression and conditional density estimation and which overc…