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Extensions of the theory of sampling to non-stationary random processes have been made by Gardner [27, 35], Garcia et al. [36] and others.
Current theory of sampling for BWSDCEs does not directly address the issue of minimum sample size in terms of reliability of the parameter estimates produced in the design.
The classical application of theory of sampling (TOS) is univariate.
We use both techniques from the theory of sampling in Paley Wiener spaces, and classical results from analytic number theory.
The present exposé shows how process sampling based on the Theory of Sampling (TOS) constitutes a new asset for regulatory compliance, providing procedures that suppress hitherto adverse sampling errors.
The Theory of Sampling (TOS) provides a description of all errors involved in sampling of heterogeneous materials as well as all necessary tools for their evaluation, elimination and/or minimization.
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Since the mid-1950s, thase has been a well-developed theory of sample survey design inference embracing complex designs with stratification and unequal probabilities.
Later, they also proposed a distributed algorithm, aSynchrornous distributEd powEr contRol (SEER) in [20], based on the theory of Gibbs sampling.
As to the status of sampling theory in the year 1975 the reader may consult [21, pp. 29 30].
This paper may be seen as in part a review covering basics of sampling theory in a different light.
That's because, from the point of sampling theory, panel data are capable of being viewed as repeated sampling of massive samples.
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