방법 비교

	적응 가중 표본 추출	가중 표본 추출
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	1990s–2000s	1940s–1952 (formalized in large-scale government survey work and the Horvitz-Thompson estimator)
창시자	Building on Thompson (1990) adaptive sampling and classical importance-weighting; adaptive weighting formalised across survey and Monte Carlo literature	Morris H. Hansen, William N. Hurwitz; D. G. Horvitz and D. J. Thompson (theoretical framework)
유형	Probabilistic sampling procedure	Probability sampling design
원전	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407
별칭	AWS, adaptive importance sampling, sequential adaptive weighting, dynamic weighted sampling	probability proportional to size sampling, PPS sampling, unequal probability sampling, importance sampling
관련	6	6
요약	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.	Weighted sampling is a probability-based design in which units are selected with unequal probabilities proportional to a known auxiliary measure of size or importance. Sampling weights — the inverse of inclusion probabilities — are applied during analysis so that each sampled unit correctly represents the population units it stands for. The approach underpins large-scale government, health, and social surveys where simple random sampling would be inefficient.