Consistency and bandwidth selection for dependent data in non-parametric functional data analysis

Simon Peter Müller
Besides a introduction this dissertation contains three more chapters. In the following paragraphs we will give a short summary for each of them. In Chapter 2 we examine non-parametric regression for α-mixing functional data. A method for estimating the regression function m(x) is the k-nearest neighbour kernel estimate. We prove that the k-NN kernel estimate is pointwise almost complete consistent for α-mixing data and we present, for two different assumptions on the covariance term, the...
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