The University of Arizona

Upcoming Events

Tuesday, August 21, 2018

Quantitative Biology Colloquium

Organizational Meeting
Location: Math 402
Presenter: Joe Watkins, Department of Mathematices & Tim Secomb Department of Physiology, University of Arizona

Wednesday, August 22, 2018

Thursday, August 23, 2018

Modeling and Computation Seminar

Organizational Meeting
Location: Math 402
Presenter: Andrew Gillette, Program in Applied Mathematics, University of Arizona

Math Circle

Organizational Meeting
Location: ENR 2 S395

Friday, August 24, 2018

Program in Applied Mathematics Colloquium

Computational methods for the study of stochastic dynamics with small noise
Location: Math 402
Presenter: Maria Cameron, Department of Mathematics, University of Maryland

Tuesday, August 28, 2018

Algebra and Number Theory Seminar

Determinants of Power Sum Matrices and a Hilbert Matrix
Location: ENR2 S395
Presenter: Dan Madden, U of A

Thursday, September 6, 2018

Modeling and Computation Seminar

Teaching Dynamics to Biology Freshmen: a Modeling Approach
Location: Math 402
Presenter: Alan Garfinkel, Graduate Programs in Bioscience, UCLA

Friday, September 7, 2018

Program in Applied Mathematics Colloquium

Bifurcation Theory and the Dynamics of Cardiac Arrhythmias
Location: Math 501
Presenter: Alan Garfinkel, Graduate Programs in Bioscience, UCLA

Monday, September 10, 2018

Statistics GIDP Colloquium

Optimal Penalized Function-on-Function Regression

Many scientific studies collect data where the response and predictor variables are both functions of time, location, or some other covariate. Understanding the relationship between these functional variables is a common goal in these studies. Motivated from two real-life examples, we present a function-on-function regression model that can be used to analyze such kind of functional data. Our estimator of the 2D coefficient function is the optimizer of a form of penalized least squares where the penalty enforces a certain level of smoothness on the estimator. Our first result is the representer theorem which states that the exact optimizer of the penalized least squares actually resides in a data-adaptive finite-dimensional subspace although the optimization problem is defined on a function space of infinite dimensions. This theorem then allows us an easy incorporation of the Gaussian quadrature into the optimization of the penalized least squares, which can be carried out through standard numerical procedures. We also show that our estimator achieves the minimax convergence rate in mean prediction under the framework of function-on-function regression. Extensive simulation studies demonstrate the numerical advantages of our method over the existing ones, where a sparse functional data extension is also introduced. The proposed method is then applied to our motivating examples of the benchmark Canadian weather data and a histone regulation study. 

Location: ENR2 S395
Presenter: Xiaoxiao Sun, University of Arizona

Friday, September 28, 2018

Mathematics Education Seminar

AZ Noyce MaTh Seminar
Location: ENR2 S395
Presenter: Sherard Robbins, UA Assistant Director for Equity and Student Engagement

Friday, October 26, 2018

Friday, November 30, 2018

Mathematics Education Seminar

AZ Noyce MaTh Seminar
Location: ENR2 S395
Presenter: Michelle Higgins, Associate Director UA STEM Learning Center
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