“Estimating a Covariance Function from Fragments of Functional Data,” Aurore Delaigle, University of Melbourne

Event time: 
Thursday, October 22, 2020 - 5:00pm to 6:15pm
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Event description: 


Abstract: Functional data are often observed only partially, in the form of fragments. In that case, the standard approaches for estimating the covariance function do not work because entire parts of the domain are completely unobserved. In previous work, Delaigle and Hall (2013, 2016) have suggested ways of estimating the covariance function, based for example on Markov assumptions. In this work we take a completely different approach which does not rely on such assumptions. We show that, using a tensor product approach, it is possible to reconstruct the covariance function using observations located only on the diagonal of its domain.

Aurore Delaigle is a Professor of Statistics in the School of Mathematics and Statistics at the University of Melbourne. Professor Delaigle is internationally known for her innovative contributions to mathematical statistics. With a unifying theme of nonparametric methods, she has made transformative contributions in several statistical subfields, including functional data analysis and measurement error. Delaigle is a theoretician with a remarkable ability to communicate complex and abstract concepts in an understandable manner. She has received many prestigious awards, including the 2017 Snedecor Award from the Committee of Presidents of Statistical Societies in the United States which cited her “fundamental and ground-breaking contributions.”

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The series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.