Please join us online for a talk by Dr. . He will present 鈥�Multiple imputation in functional regression with applications to EEG data in a depression study.鈥� on Wednesday, October 28, 2026 at 1:30 pm Central Time. Additional details coming soon. For access to this seminar, contact series administrator Cierra Streeter.
Adam Ciarleglio, PhD, is an associate professor in the Department of Biostatistics and Bioinformatics at the George Washington University Milken Institute School of Public Health, where he also directs the Biostatistics and Epidemiology Consulting Service. He received his PhD in biostatistics from Columbia University and completed postdoctoral training in biostatistics at the New York University School of Medicine. His research focuses on developing and applying methods in functional data analysis, missing data, machine learning, and precision treatment, particularly for studies involving complex biomedical data such as neuroimaging and electroencephalography. His collaborative research spans mental health, brain aging and dementia, and a broad range of other clinical and public health applications. 鈥�听
Abstract:
Missing data are a common problem in biomedical research.听 Valid approaches for addressing the problem of missing data have been proposed and are regularly implemented in applications where the data are exclusively scalar-valued.听 With advances in technology and data storage, biomedical studies now frequently collect both scalar and functional data, both of which may be subject to missingness.听 However, little work has been done to deal with completely missing functional data.听 We tackle this problem by proposing extensions of multiple imputation and Rubin鈥檚 Rules that accommodate both scalar and functional data.听 We present results from a simulation study showing the performance of our proposed extensions with respect to fidelity and estimation.鈥�疻e also present results from a study in which we apply our proposed extensions in the context of fitting a function-on-scalar regression model relating characteristics derived from electroencephalography to depression status.听