Publications

My full publication list is available on Google Scholar and in my CV. Most papers are also available as preprints on arXiv. All manuscripts are available upon request.

Recent Works

• Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random L. Testa, E.H. Kennedy, and M. Reimherr  ·  2025 Develops doubly-robust estimators for difference-in-differences designs when outcome data are partially missing. Combines semiparametric efficiency theory with modern causal inference methods.
• Doubly-Robust Functional Average Treatment Effect Estimation L. Testa, T. Boschi, F. Chiaramonte, E.H. Kennedy, and M. Reimherr  ·  Journal of Causal Inference, 2026 Extends doubly-robust causal inference to settings where outcomes are functional observations. Connects functional data analysis with modern semiparametric causal methods.
• Smoothness Adaptive Hypothesis Transfer Learning H. Lin and M. Reimherr  ·  ICML, 2024 Proposes adaptive transfer learning methods that automatically adjust to unknown smoothness differences between source and target domains. Achieves minimax-optimal rates without prior knowledge of the transfer difficulty.
• Pure Differential Privacy for Functional Summaries via a Laplace-like Process H. Lin and M. Reimherr  ·  Journal of Machine Learning Research, 2024 Introduces a new privacy mechanism for releasing functional data summaries under pure differential privacy. Provides strong theoretical guarantees while remaining practically tractable.
• M²AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding S. Alnegheimish, Z. He, A. Chandrayan, A. Pradhan, L. D’Angelo, and M. Reimherr  ·  AISTATS, 2025 Tackles anomaly detection across heterogeneous multi-sensor systems by combining global anomaly scoring with calibrated detection thresholds. Demonstrates strong empirical performance on real-world industrial datasets.

Selected Works

• Introduction to Functional Data Analysis P. Kokoszka and M. Reimherr  ·  Chapman & Hall / CRC Press, 2017  ·  834 citations The leading introductory textbook for the field, widely adopted in graduate statistics courses. Covers core theory, methods, and R-based applications, with advanced chapters for PhD-level work.
• Break Detection in the Covariance Structure of Multivariate Time Series Models A. Aue, S. Hörmann, L. Horváth, and M. Reimherr  ·  Annals of Statistics, 2009  ·  510 citations Develops tests for detecting structural breaks in the covariance structure of multivariate time series. A foundational paper in change-point analysis with wide applications in economics, finance, and environmental science.
• Determining the Order of the Functional Autoregressive Model P. Kokoszka and M. Reimherr  ·  Journal of Time Series Analysis, 2013  ·  122 citations Proposes formal statistical tests for selecting the order of functional autoregressive models, providing a principled approach to model selection in functional time series analysis.
• The Function-on-Scalar LASSO with Applications to Longitudinal GWAS R.F. Barber, M. Reimherr, and T. Schill  ·  Electronic Journal of Statistics, 2017  ·  76 citations Introduces penalized regression for simultaneous variable selection and estimation when the response is a functional observation. Scales to high-dimensional genomic settings and applied to longitudinal GWAS data.
• Nonlinear Functional Modeling Using Neural Networks A.R. Rao and M. Reimherr  ·  Journal of Computational and Graphical Statistics, 2023  ·  77 citations Extends functional data analysis to nonlinear functional regression using neural networks, bridging classical FDA methodology with modern machine learning. Demonstrates strong empirical performance on complex real-data applications.