24 citations · 31 across the 11 of their papers we have counts for
11 papers
A Comprehensive Bayesian Approach to Entity Resolution for Data with Multiple Truths
Hyungjoon Kim, Andee Kaplan, Matthew D. Koslovsky
In many applications, from government to ecology, integrating data from diverse and noisy sources is critical for downstream inference. However, a unique identifier to link records…
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…
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…
A Bayesian Nonparametric Approach for Clustering Functional Trajectories over Time
Mingrui Liang, Matthew D. Koslovsky, Emily T. Hebert +2
Functional concurrent, or varying-coefficient, regression models are commonly used in biomedical and clinical settings to investigate how the relation between an outcome and observ…
A Unified Bayesian Framework for Modeling Measurement Error in Multinomial Data
Matthew D. Koslovsky, Andee Kaplan, Victoria A. Terranova +1
Measurement error in multinomial data is a well-known and well-studied inferential problem that is encountered in many fields, including engineering, biomedical and omics research,…
A Bayesian Zero-Inflated Dirichlet-Multinomial Regression Model for Multivariate Compositional Count Data
Matthew D. Koslovsky
The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants h…
Melding Wildlife Surveys to Improve Conservation Inference
Justin Van Ee, Christian Hagen, David Pavlacky +3
Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several d…
A Bayesian Joint Model for Compositional Mediation Effect Selection in Microbiome Data
Jingyan Fu, Matthew D. Koslovsky, Andreas M. Neophytou +1
Analyzing multivariate count data generated by high-throughput sequencing technology in microbiome research studies is challenging due to the high-dimensional and compositional str…
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…
A Bayesian Time-Varying Effect Model for Behavioral mHealth Data
Matthew D. Koslovsky, Emily T. Hebert, Michael S. Businelle +1
The integration of mobile health (mHealth) devices into behavioral health research has fundamentally changed the way researchers and interventionalists are able to collect data as…
A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes
Matthew D. Koslovsky, Kristi L. Hoffman, Carrie R. Daniel +1
One of the major research questions regarding human microbiome studies is the feasibility of designing interventions that modulate the composition of the microbiome to promote heal…