• Corpus ID: 14995778

On recovering missing values in a pathwise setting

@article{Dokuchaev2016OnRM,
  title={On recovering missing values in a pathwise setting},
  author={Nikolai Dokuchaev},
  journal={ArXiv},
  year={2016},
  volume={abs/1604.04967}
}
  • N. Dokuchaev
  • Published 18 April 2016
  • Computer Science, Mathematics
  • ArXiv
The paper suggests a frequency criterion of error-free recoverability of a missing value for sequences, i.e., discrete time processes, in a pathwise setting, without using probabilistic assumptions on the ensemble. This setting targets situations where we deal with a sole sequence that is deemed to be unique and such that we cannot rely on statistics collected from other similar samples. A missing value has to be recovered using the intrinsic properties of this sole sequence. In the paper… 
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5 Citations
Background Citations
3
Methods Citations
1
Results Citations
1

5 Citations

On exact and optimal recovering of missing values for sequences
  • N. Dokuchaev
  • Computer Science, Mathematics
    Signal Process.
  • 2017
On sampling theorem with sparse decimated samples: exploring branching spectrum degeneracy
The paper investigates possibility of recovery of sequences from their decimated subsequences. It is shown that this recoverability is associated with certain spectrum degeneracy of a new kind, and…
Optimal data recovery and forecasting with dummy long-horizon forecasts
TLDR
A method of recovering missing values for sequences, including sequences with a multidimensional index, based on optimal approximation by processes featuring spectrum degeneracy is suggested, without using probabilistic assumptions on the ensemble.
On recovery of signals with single point spectrum degeneracy
TLDR
Recovery problem for discrete time signals with a finite number of missing values for signals with Z-transform vanishing with a certain rate at a single point is studied.
On sampling theorem with sparse decimated samples
TLDR
Application of the classical sampling Nyquist-Shannon-Kotelnikov theorem to general non-bandlimited functions allows an arbitrarily close uniform approximation by functions that are uniquely defined by the extremely sparse subsamples representing arbitrarily small fractions of one-sided equidist sample series with fixed oversampling parameter.

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