Michael J. Daniels | DeepAI

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∙ 05/09/2023

Dirichlet process mixture models for the Analysis of Repeated Attempt Designs

In longitudinal studies, it is not uncommon to make multiple attempts to...

0 Michael J. Daniels, et al. ∙

∙ 05/08/2023

A Bayesian Non-parametric Approach for Causal Mediation with a Post-treatment Confounder

We propose a new Bayesian non-parametric (BNP) method for estimating the...

0 Woojung Bae, et al. ∙

∙ 05/02/2023

Truncation Approximation for Enriched Dirichlet Process Mixture Models

Enriched Dirichlet process mixture (EDPM) models are Bayesian nonparamet...

0 Natalie Burns, et al. ∙

∙ 08/29/2022

A Bayesian nonparametric approach for causal inference with multiple mediators

Mediation analysis with contemporaneously observed multiple mediators is...

0 Samrat Roy, et al. ∙

∙ 08/21/2022

Flexible evaluation of surrogacy in Bayesian adaptive platform studies

Trial level surrogates are useful tools for improving the speed and cost...

0 Michael C Sachs, et al. ∙

∙ 01/09/2022

Information Borrowing in Regression Models

Model development often takes data structure, subject matter considerati...

0 Amy Zhang, et al. ∙

∙ 12/28/2021

Variable Selection Using Bayesian Additive Regression Trees

Variable selection is an important statistical problem. This problem bec...

0 Chuji Luo, et al. ∙

∙ 06/28/2021

BNPqte: A Bayesian Nonparametric Approach to Causal Inference on Quantiles in R

In this article, we introduce the BNPqte R package which implements the ...

0 Chuji Luo, et al. ∙

∙ 01/18/2021

Inference for BART with Multinomial Outcomes

The multinomial probit Bayesian additive regression trees (MPBART) frame...

0 Yizhen Xu, et al. ∙

∙ 11/29/2020

Approximate Cross-validated Mean Estimates for Bayesian Hierarchical Regression Models

We introduce a novel procedure for obtaining cross-validated predictive ...

0 Amy X. Zhang, et al. ∙

∙ 11/24/2020

A Bayesian semi-parametric approach for inference on the population partly conditional mean from longitudinal data with dropout

Studies of memory trajectories using longitudinal data often result in h...

0 Maria Josefsson, et al. ∙

∙ 11/01/2020

Informed Pooled Testing with Quantitative Assays

Pooled testing is widely used for screening for viral or bacterial infec...

0 Tao Liu, et al. ∙

∙ 02/27/2019

Bayesian semi-parametric G-computation for causal inference in a cohort study with non-ignorable dropout and death

Causal inference with observational longitudinal data and time-varying e...

0 Maria Josefsson, et al. ∙

∙ 01/03/2019

Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions

Pollutant emissions from coal-burning power plants have been deemed to a...

0 Chanmin Kim, et al. ∙

∙ 12/16/2018

Classification using Ensemble Learning under Weighted Misclassification Loss

Binary classification rules based on covariates typically depend on simp...

0 Yizhen Xu, et al. ∙

∙ 05/18/2018

A Bayesian Parametric Approach to Handle Missing Longitudinal Outcome Data in Trial-Based Health Economic Evaluations

Trial-based economic evaluations are typically performed on cross-sectio...

0 Andrea Gabrio, et al. ∙

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