Detecting relevant differences in the covariance operators of functional time series - a sup-norm approach
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Date
2020
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Abstract
In this paper we propose statistical inference tools for the covariance operators of functional
time series in the two sample and change point problem. In contrast to most of
the literature the focus of our approach is not testing the null hypothesis of exact equality
of the covariance operators. Instead we propose to formulate the null hypotheses in the
form that "the distance between the operators is small", where we measure deviations by
the sup-norm. We provide powerful bootstrap tests for these type of hypotheses, investigate
their asymptotic properties and study their finite sample properties by means of a
simulation study.
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Keywords
covariance operator, bootstrap, Banach spaces, relevant hypotheses, CUSUM, change point problems, two sample problems, functional time series