A general purpose unequal probability sampling plan

By M. Chao. 1982

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Peter Bailis
2. D. Narayanan
3. Edward Gan
4. Xiao Lin
5. Yuchen Zhang
6. Kexin Rong
7. Sahaana Suri
8. Samuel Madden

Citations in the corpus

• Information-theoretic Online Memory Selection for Continual Learning (2022, neutral): “Consequently, one can adopt WRS to favor specific points according to the chosen importance weight.”
• MacroBase: Analytic Monitoring for the Internet of Things (2016, methodology): “In MacroBase, we leverage techniques from the sketching and streaming literature [14], specifically in our AMC sketch [16, 37] and FDR sampler [12,19].”
• A jackknife variance estimator for unequal probability sampling (2005, background): “Deville and Särndal (1992)), which employ auxiliary population information may often be expressible as functions of means if the function g.”
• MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “As in Chao’s sampler, in the event of extreme decay, “overweight” items with relative insertion probability k cw > 1 are always retained in the reservoir until their insertion probability falls below 1, at which point they are inserted normally.”
• Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization (2014, background): “More concretely, there is an efficient sampling-without-replacement algorithm (Chao, 1982) which adds each remaining index i to the set K with probability proportional to 1−α n + α‖ai‖2 nR .”