Sequential online subsampling for thinning experimental designs

@article{Pronzato2020SequentialOS,
  title={Sequential online subsampling for thinning experimental designs},
  author={Luc Pronzato and Haiying Wang},
  journal={Journal of Statistical Planning and Inference},
  year={2020}
}
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2 Citations
Background Citations
1
Methods Citations
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2 Citations

Citation Type

Citation Type

A Review on Optimal Subsampling Methods for Massive Datasets
TLDR
The optimal subsampling methods have been investigated to include logistic regression models, softmax regressors, generalized linear models, quantile 12 regression Models, and quasi-likelihood estimation.
Maximum sampled conditional likelihood for informative subsampling
TLDR
The asymptotic normality of the MSCLE is established and it is proved that its asymPTotic variance covariance matrix is the smallest among a class of asymptonically unbiased estimators, including the inverse probability weighted estimator.

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