Sparse and Smooth Function Estimation in Reproducing Kernel Hilbert Spaces

Seminar Series

Friday, November 19, 2021 - 03:30
Hao Helen Zhang Phd

Abstract:  Curse of dimensionality refers to sparse phenomena of high-dimensional data, and it presents substantial challenges in the theory and computation of nonparametric models. In this talk I will present a class of regularization operators which enables sparse and smooth estimation of multi-dimensional functions in reproducing kernel Hilbert spaces. The operator leads to a unified framework for model selection to enhance the accuracy and interpretability of a variety of nonparametric models, including generalized additive models, partially linear models, and functional additive models. We discuss theoretical properties of the estimators and demonstrate their empirical performance in real-world examples.

Spearker: Hao Helen Zhang, PhD
Chair & Professor of Statistics
Department of Mathematics
University of Arizona

Bio:  Hao Helen Zhang is a Professor of Department of Mathematics at University of Arizona, as well as a faculty member of Statistics Graduate Interdisciplinary Program (GIDP). Dr. Zhang obtained a Ph.D. in Statistics from University of Wisconsin at Madison in 2002. She was assistant and associate professor of Statistics at North Carolina State University 2002-2011. Dr. Zhang’s research areas include statistical machine learning, high-dimensional data analysis, nonparametric smoothing, and biomedical data analysis. Her research was funded by NSF, NIH, NSA, including a NSF CAREER Award. Dr. Zhang is currently Associate Editor of Journal of American Statistical Association, Journal of Computational and Graphical Statistics, and Statistical Analysis and Data Mining. She is a Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, as well as elected member of the International Statistical Institute. 

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Meeting ID: 923 9738 2385
Passcode: 425966

Sponsored by Duke Department of Statistical Science  
Cosponsored by Duke Department of Biostatistics & Bioinformatics