24 citations · 49 across the 10 of their papers we have counts for
5 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…
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…
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…
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…
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…