Abstract
Instead of observing a complete segment of a process on an interval, it is evidently desirable to consider suitable subsets, preferably countable ones, if they present the essential characteristics of the process on the bigger segment. A basic result in this direction for second order processes is the one due independently to Kotel’nikov and Shannon, and we present some results of this type for the stationary as well as some general processes, in Section 1.
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© 2014 Springer International Publishing Switzerland
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Rao, M.M. (2014). Sampling and Regression for Processes. In: Stochastic Processes - Inference Theory. Springer Monographs in Mathematics. Springer, Cham. https://doi.org/10.1007/978-3-319-12172-7_6
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DOI: https://doi.org/10.1007/978-3-319-12172-7_6
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12171-0
Online ISBN: 978-3-319-12172-7
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