1 citations · 1 across the 13 of their papers we have counts for
14 papers
Clarifying identification and estimation of treatment effects in the Sequential Parallel Comparison Design
Benjamin Stockton, Michele Santacatterina, Soutrik Mandal +8
Sequential parallel comparison design (SPCD) clinical trials aim to adjust active treatment effect estimates for placebo response to minimize the impact of placebo responders on th…
Bayesian Mixed-Effects Models for Multilevel Two-way Functional Data: Applications to EEG Experiments
Xiaomeng Ju, Thaddeus Tarpey, Hyung G Park
In multi-condition EEG experiments, brain activity is recorded as subjects perform various tasks or are exposed to different stimuli. The recorded signals are commonly transformed…
Projection-pursuit Bayesian regression for symmetric matrix predictors
Xiaomeng Ju, Hyung G. Park, Thaddeus Tarpey
This paper develops a novel Bayesian approach for nonlinear regression with symmetric matrix predictors, often used to encode connectivity of different nodes. Unlike methods that v…
Trajectory-Based Individualized Treatment Rules
Lanqiu Yao, Thaddeus Tarpey
A core component of precision medicine research involves optimizing individualized treatment rules (ITRs) based on patient characteristics. Many studies used to estimate ITRs are l…
Extracting Scalar Measures from Curves
Lanqiu Yao, Thaddeus Tarpey
The ability to order outcomes is necessary to make comparisons which is complicated when there is no natural ordering on the space of outcomes, as in the case of functional outcome…
Bayesian scalar-on-network regression with applications to brain functional connectivity
Xiaomeng Ju, Hyung G. Park, Thaddeus Tarpey
This paper presents a Bayesian regression model relating scalar outcomes to brain functional connectivity represented as symmetric positive definite (SPD) matrices. Unlike many pro…
K-Tensors: Clustering Positive Semi-Definite Matrices
Hanchao Zhang, Xiaomeng Ju, Baoyi Shi +2
This paper presents a new clustering algorithm for symmetric positive semi-definite (SPSD) matrices, called K-Tensors. The method identifies structured subsets of the SPSD cone cha…
Bayesian index models for heterogeneous treatment effects
Hyung Park, Danni Wu, Eva Petkova +2
The general idea of this article is to develop a Bayesian model with a flexible link function connecting an exponential family treatment response to a linear combination of covaria…
A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules
Lanqiu Yao, Thaddeus Tarpey
A pressing challenge in medical research is to identify optimal treatments for individual patients. This is particularly challenging in mental health settings where mean responses…
Unsupervised Bayesian classification for models with scalar and functional covariates
Nancy L. Garcia, Mariana Rodrigues-Motta, Helio S. Migon +5
We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of L components of a mixture model. This process can…
A single-index model with a surface-link for optimizing individualized dose rules
Hyung Park, Eva Petkova, Thaddeus Tarpey +1
This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regressi…
Functional additive models for optimizing individualized treatment rules
Hyung Park, Eva Petkova, Thaddeus Tarpey +1
A novel functional additive model is proposed which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large numbe…
A constrained sparse additive model for treatment effect-modifier selection
Hyung Park, Eva Petkova, Thaddeus Tarpey +1
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This paper develops a sparse additive model focused on estimation…
Sufficient Dimension Reduction for Interactions
Hyung Park, Eva Petkova, Thaddeus Tarpey +1
Dimension reduction lies at the heart of many statistical methods. In regression, dimension reduction has been linked to the notion of sufficiency whereby the relation of the respo…