Sparse and Smooth Function Estimation in Reproducing Kernel Hilbert Spaces

November 19, 2021
3:30 pm to 4:30 pm

Event sponsored by:

Statistical Science
Biostatistics and Bioinformatics


StatSci Seminar Coordinator



Helen Zhang, from Department of Mathematics, University of Arizona

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. Seminars will be held weekly on Fridays 3:30 - 4:30 pm on Zoom. After the seminar, there will be a (virtual) meet-and-greet session to interact with the speaker. Please use the chat on Zoom to ask questions to the speaker. A moderator will collect questions throughout the talk and ask the speaker at appropriate times. Join Zoom Meeting Meeting ID: 923 9738 2385 Passcode: 425966

Joint Seminar