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Title: Spectral algorithms for functional linear regression Authors:  Jun Fan - Hong Kong Baptist University (Hong Kong) [presenting]
Abstract: Functional data analysis is concerned with inherently infinite dimensional data such as curves or images. It attracts more and more attentions because of its successful applications in many areas such as neuroscience and econometrics. We consider a class of regularization methods called spectral algorithms for functional linear regression within the framework of reproducing kernel Hilbert space. The proposed estimators can achieve the minimax optimal rates of convergence. Despite of the infinite dimensional nature of functional data, we show that the algorithms are easily implementable.