Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Volterra bootstrap: Resampling higher-order statistics for strictly
  stationary univariate time series   We are concerned with nonparametric hypothesis testing of time series
functionals. It is known that the popular autoregressive sieve bootstrap is, in
general, not valid for statistics whose (asymptotic) distribution depends on
moments of order higher than two, irrespective of whether the data come from a
linear time series or a nonlinear one. Inspired by nonlinear system theory we
circumvent this non-validity by introducing a higher-order bootstrap scheme
based on the Volterra series representation of the process. In order to
estimate coefficients of such a representation efficiently, we rely on the
alternative formulation of Volterra operators in reproducing kernel Hilbert
space. We perform polynomial kernel regression which scales linearly with the
input dimensionality and is independent of the degree of nonlinearity. We
illustrate the applicability of the suggested Volterra-representation-based
bootstrap procedure in a simulation study where we consider strictly stationary
linear and nonlinear processes.

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The categories of this paper are: comp-gas
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int