22 citations · 30 across the 4 of their papers we have counts for
5 papers
Unweighted estimation based on optimal sample under measurement constraints
Jing Wang, HaiYing Wang, Shifeng Xiong
To tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsa…
Most Likely Optimal Subsampled Markov Chain Monte Carlo
Guanyu Hu, HaiYing Wang
Markov Chain Monte Carlo (MCMC) requires to evaluate the full data likelihood at different parameter values iteratively and is often computationally infeasible for large data sets.…
Sequential online subsampling for thinning experimental designs
Luc Pronzato, HaiYing Wang
We consider a design problem where experimental conditions (design points ) are presented in the form of a sequence of i.i.d.\ random variables, generated with an unknown prob…
Optimal subsampling for quantile regression in big data
HaiYing Wang, Yanyuan Ma
We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsamp…
Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm
HaiYing Wang
The information-based optimal subdata selection (IBOSS) is a computationally efficient method to select informative data points from large data sets through processing full data by…