Year: 2018
Type: preprint
Abstract: This article deals with the problem of functional classification for L2-valued random covariates when some of the covariates may have missing or unobservable fragments. Here, it is allowed for both the training sample as well as the new unclassified observation to have missing fragments in their functional covariates. Furthermore, unlike most previous results in the literature, where covariate fragments are typically assumed to be missing completely at random, we do not impose any such assumptions here. Given the observed segments of the curves, we construct a kernel-type classifier which is quite straightforward to implement in practice. We also establish the strong consistency of the proposed classifier and provide some numerical examples to assess its performance in practice.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2897521016