270 citations · 574 across the 13 of their papers we have counts for
13 papers
Independence-Encouraging Subsampling for Nonparametric Additive Models
Yi Zhang, Lin Wang, Xiaoke Zhang +1
The additive model is a popular nonparametric regression method due to its ability to retain modeling flexibility while avoiding the curse of dimensionality. The backfitting algori…
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…
Approximating Partial Likelihood Estimators via Optimal Subsampling
Haixiang Zhang, Lulu Zuo, HaiYing Wang +1
With the growing availability of large-scale biomedical data, it is often time-consuming or infeasible to directly perform traditional statistical analysis with relatively limited…
Maximum sampled conditional likelihood for informative subsampling
HaiYing Wang, Jae Kwang Kim
Subsampling is a computationally effective approach to extract information from massive data sets when computing resources are limited. After a subsample is taken from the full dat…
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.…
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data
Jun Yu, HaiYing Wang, Mingyao Ai +1
Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with r…
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…
Optimal Subsampling Algorithms for Big Data Regressions
Mingyao Ai, Jun Yu, Huiming Zhang +1
To fast approximate maximum likelihood estimators with massive data, this paper studies the Optimal Subsampling Method under the A-optimality Criterion (OSMAC) for generalized line…
More Efficient Estimation for Logistic Regression with Optimal Subsample
HaiYing Wang
In this paper, we propose improved estimation method for logistic regression based on subsamples taken according the optimal subsampling probabilities developed in Wang et al. 2018…
Information-Based Optimal Subdata Selection for Big Data Linear Regression
HaiYing Wang, Min Yang, John Stufken
Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large data sets due to computat…
Optimal Subsampling for Large Sample Logistic Regression
HaiYing Wang, Rong Zhu, Ping Ma
For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary…