Skip to main content
Skip to main menu


Kehui Chen

University of California, Davis

In this talk, I will first present a method for conditional distribution and quantile estimation when predictors take values in a functional space, which is an extension of the usual functional mean regression. The study is motivated and illustrated by an application to the assessment of children’s growth patterns. The proposed method is supported by theory and is shown to perform well in simulations. An extension of the proposed conditional approach to model the more complex case when responses are also functions will be briefly discussed. In the second part, I will adopt a broader perspective, and demonstrate how the ‘blessings of dimensionality’ principle motivates the `Stringing’ method. In this approach, we represent high-dimensional data as discretized, noisy, and order-scrambled observations from a hidden stochastic process. Simulations show that this method works well in various high-dimensional settings.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.