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The goodness-of-fit statistics are based on an underlying empirical process for which large sample theory is obtained.
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The sampling theory is basic and crucial in engineering sciences.
The tuning of a nonresponse in sampling theory is addressed in one of the unsolved exercises.
The restricted isometry property condition necessary for accurate recovery of compressive sampling theory is satisfied mathematically.
A multi-rate sampling theory is proposed by Chayratsami and Wickert [8].
Sampling theory is one of the most powerful results in signal analysis.
An overview of sampling theory is provided, strategies for sampler design are examined, and calibration schemes are discussed.
A fundamental problem in sampling theory is the robustness of signal reconstruction in the presence of sampling noises.
Therefore, the probability based on the small sample theory (exact sampling theory) is calculated by the Bayesian approach with Jeffreys' noninformative prior distribution.
Sampling theory is one of the most important mathematical tools used in communication engineering since it enables engineers to reconstruct signals from some of their sampled data.
Moreover, sampling theory is one of the most important mathematical tools used in communication engineering, the Whittaker-Kotel'nikov-Shannon (WKS) sampling theorem is viewed as the fundamental result in information theory [6 8].
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