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stat.ME2026
A Bayesian Functional Concurrent Zero-Inflated Dirichlet-Multinomial Regression Model with Application to Infant Microbiome
Brody Erlandson, Ander Wilson, Matthew D. Koslovsky
The infant microbiome undergoes rapid changes in composition over time and is associated with long-term risks of conditions such as immune strength, allergy, asthma, and other heal…
stat.ME2025
A Bayesian Semiparametric Mixture Model for Clustering Zero-Inflated Microbiome Data
Suppapat Korsurat, Matthew D. Koslovsky
Microbiome research has immense potential for unlocking insights into human health and disease. A common goal in human microbiome research is identifying subgroups of individuals w…
stat.ME2022
Infinite Hidden Markov Models for Multiple Multivariate Time Series with Missing Data
Lauren Hoskovec, Matthew D. Koslovsky, Kirsten Koehler +4
Exposure to air pollution is associated with increased morbidity and mortality. Recent technological advancements permit the collection of time-resolved personal exposure data. Suc…