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| Προσαρμοστική Στρωματοποιημένη Δειγματοληψία× | Προσαρμοστική Δειγματοληψία Συμπλεγμάτων× | |
|---|---|---|
| Πεδίο | Μεθοδολογία Επισκοπήσεων | Μεθοδολογία Επισκοπήσεων |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 1990s (formal development from Thompson 1990 onward) | 1990 |
| Δημιουργός≠ | Steven K. Thompson (adaptive sampling); allocation adaptations by Salehi, Seber, and others | Steven K. Thompson |
| Τύπος | Probability-based adaptive sampling design | Probability-based adaptive sampling design |
| Θεμελιώδης πηγή | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ |
| Εναλλακτικές ονομασίες | ASS, adaptive stratified design, stratified adaptive sampling, adaptive allocation stratified sampling | ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling |
| Συναφείς | 6 | 6 |
| Σύνοψη≠ | Adaptive stratified sampling divides the population into strata and then applies an adaptive rule within each stratum: whenever an initially selected unit satisfies a pre-specified condition (e.g., a rare species is found, a variable exceeds a threshold), neighboring or related units are added to the sample. This combines the variance-reduction power of stratification with the ability to concentrate sampling effort where the phenomenon of interest is actually present. | Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups. |
| ScholarGateΣύνολο δεδομένων ↗ |
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