Sequential online subsampling for thinning experimental designs

  title={Sequential online subsampling for thinning experimental designs},
  author={Luc Pronzato and Haiying Wang},
  journal={Journal of Statistical Planning and Inference},
A Review on Optimal Subsampling Methods for Massive Datasets
The optimal subsampling methods have been investigated to include logistic regression models, softmax regression model, generalized linear models, quantile regression Models, and quasi-likelihood estimation.
Maximum sampled conditional likelihood for informative subsampling
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.


A Space-Efficient Recursive Procedure for Estimating a Quantile of an Unknown Distribution
Consider the problem of computing an estimate of a percentile or quantile of an unknown population based on a random sample of n observations. By viewing this problem as a problem in stochastic
Stochastic approximation with two time scales
Bayesian Estimation and Experimental Design in Linear Regression Models, volume 55. Teubner-Texte zur Mathematik, Leipzig
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On the sequential construction of optimum bounded designs
Analysis of recursive stochastic algorithms
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It is shown how a deterministic differential equation can be associated with the algorithm and examples of applications of the results to problems in identification and adaptive control.
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning
This work develops a novel recipe for their finite sample analysis, and provides a concentration bound, which is the first such result for a two-timescale SA, and introduces a new projection scheme, in which the time between successive projections increases exponentially.
Theory and Practice of Recursive Identification
Methods of recursive identification deal with the problem of building mathematical models of signals and systems on-line, at the same time as data is being collected. Such methods, which are also k
Theory and Practice of Recursive Identi cation
  • 1983
Information-Based Optimal Subdata Selection for Big Data Linear Regression
Theoretical results and extensive simulations demonstrate that the IBOSS approach is superior to subsampling-based methods, sometimes by orders of magnitude, and the advantages of the new approach are also illustrated through analysis of real data.