Mobile health (mHealth) methods allow researchers to monitor study participants in their natural environments in order to improve health-related outcomes through behavior change. MHealth investigators are interested in understanding the feasibility of supplementing or even replacing actively collected data with passively collected data to reduce participant burden, while also increasing...
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Awarded NSF Grant
Bayesian Sparse Dirichlet-Multinomial Models for Discovering Latent Structure in High-Dimensional Compositional Count Data
Matt Koslovsky (PI) received NSF grant “Bayesian Sparse Dirichlet-Multinomial Models for Discovering Latent Structure in High-Dimensional Compositional Count Data” funded by the Division of Mathematical Sciences program “Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences.” Motivated by data generated by high-throughput sequencing technology in omics research, this...
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Methods paper accepted in Bayesian Analysis
Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors
Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied...
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Methods paper accepted in Statistics in Medicine
A Bayesian Joint Model for Mediation Effect Selection in Compositional Microbiome Data
Analyzing multivariate count data generated by high-throughput sequencing technology in microbiome research studies is challenging due to the high-dimensional and compositional structure of the data and overdispersion. In practice, researchers are often interested in investigating how the microbiome may mediate the relation between an assigned treatment and an observed phenotypic...
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Methodology paper accepted in Biometrics
A Bayesian Zero-Inflated Dirichlet-Multinomial Regression Model for Multivariate Compositional Count Data
The Dirichlet-multinomial (DM) distribution plays a fundamental role in modern statistical methodology development and application. Recently, the DM distribution and its variants have been used extensively to model multivariate count data generated by high-throughput sequencing technology in omics research due to its ability to accommodate the compositional structure of the...
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