So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Lấy mẫu có trọng số thích ứng× | Lấy mẫu có hệ thống× | |
|---|---|---|
| Lĩnh vực | Phương pháp luận khảo sát | Phương pháp luận khảo sát |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1990s–2000s | Mid-20th century (Cochran 1953; Kish 1965) |
| Người khởi xướng≠ | Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature | William G. Cochran; formalized in survey sampling theory |
| Loại≠ | Probabilistic sampling procedure | Probability sampling design |
| Công trình gốc≠ | Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059. DOI ↗ | Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407 |
| Tên gọi khác | AWS, adaptive importance sampling, sequential adaptive weighting, dynamic weighted sampling | interval sampling, systematic random sampling, equal-interval sampling, fixed-interval sampling |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | Adaptive weighted sampling is a probabilistic sampling procedure that assigns and iteratively updates inclusion weights for population units based on observed data collected during the sampling process itself. Unlike static weighted sampling — where weights are fixed before data collection from known auxiliary information — adaptive weighting revises probabilities as new information accumulates, concentrating sampling effort on units that contribute most to estimating the target quantity. It is used in survey methodology, simulation studies, and rare-event estimation. | Systematic sampling is a probability sampling technique in which every k-th element is selected from an ordered list of the population after a random starting point. With population size N and desired sample size n, the sampling interval k = N/n is computed and one unit is chosen at random from the first interval; all subsequent units are selected by adding k repeatedly. The method is operationally simple, yields a spread-out sample, and often achieves lower variance than simple random sampling when the list has no harmful periodicity. |
| ScholarGateBộ dữ liệu ↗ |
|
|