Nonparametric modelling for functional data: selected survey and tracks for future

By Nengxiang Ling, Philippe Vieu. Statistics (Berlin), 2018

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ABSTRACT Nonparametric functional data analysis is a field whose development started some 15 years ago and there is a very extensive literature on the topic (hundreds of papers published now). The first aim of this survey is to discuss the state of art in the field through a necessarily selected, bibliographical survey. The second aim is to present a wide scope of open questions in order to promote further discussions. Our main purpose is restricted to methodological contributions and also to emphasize on kernel functional regression analysis before extending discussions in two directions: alternative techniques to kernel methods for functional regression and other statistical problems besides regression.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4b14f3b210631b4f8ef6601739dc502af58e7235