251 citations · 487 across the 35 of their papers we have counts for
15 papers · 1 filter
Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random
Lorenzo Testa, Edward H. Kennedy, Matthew Reimherr
The Difference-in-Differences (DiD) method is a fundamental tool for causal inference, yet its application is often complicated by missing data. Although recent work has developed…
Doubly-Robust Functional Average Treatment Effect Estimation
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte +2
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inf…
Functional Factor Modeling of Brain Connectivity
Kyle Stanley, Nicole Lazar, Matthew Reimherr
Many fMRI analyses examine functional connectivity, or statistical dependencies among remote brain regions. Yet popular methods for studying whole-brain functional connectivity oft…
FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions
Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte +1
Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and f…
Modern Non-Linear Function-on-Function Regression
Aniruddha Rajendra Rao, Matthew Reimherr
We introduce a new class of non-linear function-on-function regression models for functional data using neural networks. We propose a framework using a hidden layer consisting of c…
Modern Multiple Imputation with Functional Data
Aniruddha Rajendra Rao, Matthew Reimherr
This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state-of-the-art methods, wh…
Fast and Fair Simultaneous Confidence Bands for Functional Parameters
Dominik Liebl, Matthew Reimherr
Quantifying uncertainty using confidence regions is a central goal of statistical inference. Despite this, methodologies for confidence bands in Functional Data Analysis are still…
Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
Ardalan Mirshani, Matthew Reimherr
This paper presents a new methodology, called AFSSEN, to simultaneously select significant predictors and produce smooth estimates in a high-dimensional function-on-scalar linear m…
Highly Irregular Functional Generalized Linear Regression with Electronic Health Records
Justin Petrovich, Matthew Reimherr, Carrie Daymont
This work presents a new approach, called MISFIT, for fitting generalized functional linear regression models with sparsely and irregularly sampled data. Current methods do not all…
Manifold Data Analysis with Applications to High-Frequency 3D Imaging
Hyun Bin Kang, Matthew Reimherr, Mark Shriver +1
Many scientific areas are faced with the challenge of extracting information from large, complex, and highly structured data sets. A great deal of modern statistical work focuses o…
A Geometric Approach to Confidence Regions and Bands for Functional Parameters
Hyunphil Choi, Matthew Reimherr
Functional data analysis, FDA, is now a well established discipline of statistics, with its core concepts and perspectives in place. Despite this, there are still fundamental stati…
A randomness test for functional panels
Piotr Kokoszka, Matthew Reimherr, Nikolas Wölfing
Functional panels are collections of functional time series, and arise often in the study of high frequency multivariate data. We develop a portmanteau style test to determine if t…
Testing separability of space--time functional processes
Panayiotis Constantinou, Piotr Kokoszka, Matthew Reimherr
We present a new methodology and accompanying theory to test for separability of spatio-temporal functional data. In spatio-temporal statistics, separability is a common simplifyin…
Prior sample size extensions for assessing prior impact and prior--likelihood discordance
Matthew Reimherr, Xiao-Li Meng, Dan L. Nicolae
This paper outlines a framework for quantifying the prior's contribution to posterior inference in the presence of prior-likelihood discordance, a broader concept than the usual no…
On Quantifying Dependence: A Framework for Developing Interpretable Measures
Matthew Reimherr, Dan L. Nicolae
We present a framework for selecting and developing measures of dependence when the goal is the quantification of a relationship between two variables, not simply the establishment…