A general stream sampling design

Bardia Panahbehagh (), Raphaël Jauslin and Yves Tillé
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Bardia Panahbehagh: Kharazmi University
Raphaël Jauslin: Kharazmi University
Yves Tillé: University of Neuchâtel

Computational Statistics, 2024, vol. 39, issue 6, No 1, 2899-2924

Abstract: Abstract With the emergence of the big data era, the need for sampling methods that select samples based on the order of the observed units is felt more than ever. In order to meet this necessity, a new sequential unequal probability sampling method is proposed. The decision to select or not each unit is made based on the order in which the units appear. A variant of this method allows a selection of a sample from a stream. This method consists in using sliding windows which are a kind of strata of controllable size. This method also allows the sample to be spread in a controlled manner throughout the population. A special case of the method with windows of size one leads to deciding on each sampling unit immediately after observing it. The implementation of size one windows is simple and will be presented here based on an algorithm with a single condition. Also, by selecting the windows of size two, we will have one of the optimal stream sampling methods, which results in a well-spread stream sample with positive second-order inclusion probabilities.

Keywords: Stream sampling algorithms; Inclusion probability; Flow sampling; Stratified stream sampling; Window (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01408-7

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