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20202023
most citedA maximin optimal approach for sampling designs in two-phase studies

1 citations · 1 across the 2 of their papers we have counts for

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stat.ME2023★ 1 cited

A maximin optimal approach for sampling designs in two-phase studies

Ruoyu Wang, Qihua Wang, Wang Miao

Data collection costs can vary widely across variables in data science tasks. Two-phase designs can be employed to save data collection costs. This paper considers the two-phase st…

stat.ME2023

A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data

Miaomiao Su, Qihua Wang, Ruoyu Wang

The maximum likelihood estimation is computationally demanding for large datasets, particularly when the likelihood function includes integrals. Subsampling can reduce the computat…

stat.ME2021

A robust fusion-extraction procedure with summary statistics in the presence of biased sources

Ruoyu Wang, Qihua Wang, Wang Miao

Information from various data sources is increasingly available nowadays. However, some of the data sources may produce biased estimation due to commonly encountered biased samplin…

stat.ME2021

Distributed nonparametric regression imputation for missing response problems with large-scale data

Ruoyu Wang, Miaomiao Su, Qihua Wang

Nonparametric regression imputation is commonly used in missing data analysis. However, it suffers from the ``curse of dimension". The problem can be alleviated by the explosive sa…

stat.ME2020

A Convex Programming Solution Based Debiased Estimator for Quantile with Missing Response and High-dimensional Covariables

Miaomiao Su, Qihua Wang

This paper is concerned with the estimating problem of response quantile with high dimensional covariates when response is missing at random. Some existing methods define root-n co…