Abstract:
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Multidimensional functional data are becoming more common in various domains such as climate studies, neuroimaging, and chemometrics. In this talk, I will present a nonparametric covariance function estimation approach under the framework of reproducing kernel Hilbert spaces (RKHS) that can handle both sparse and dense functional data. It has low-rank structures in both eigen-components of covariance function and marginal structures. Also, I will discuss the corresponding numerical results and the unified convergence theory.
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