79 citations · 88 across the 11 of their papers we have counts for
11 papers
Multiple bias-calibration for adjusting selection bias of non-probability samples using data integration
Zhonglei Wang, Shu Yang, Jae Kwang Kim
Valid statistical inference is challenging when the sample is subject to unknown selection bias. Data integration can be used to correct for selection bias when we have a parallel…
Transductive Matrix Completion with Calibration for Multi-Task Learning
Hengfang Wang, Yasi Zhang, Xiaojun Mao +1
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful…
Probability Weighted Clustered Coefficients Regression Models in Complex Survey Sampling
Mingjun Gang, Xin Wang, Zhonglei Wang +1
Regression analysis is commonly conducted in survey sampling. However, existing methods fail when the relationships vary across different areas or domains. In this paper, we propos…
Nonparametric augmented probability weighting with sparsity
Xin He, Xiaojun Mao, Zhonglei Wang
Nonresponse frequently arises in practice, and simply ignoring it may lead to erroneous inference. Besides, the number of collected covariates may increase as the sample size in mo…
Functional Calibration under Non-Probability Survey Sampling
Zhonglei Wang, Xiaojun Mao, Jae Kwang Kim
Non-probability sampling is prevailing in survey sampling, but ignoring its selection bias leads to erroneous inferences. We offer a unified nonparametric calibration method to est…
Nonparametric Feature Selection by Random Forests and Deep Neural Networks
Xiaojun Mao, Liuhua Peng, Zhonglei Wang
Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and usele…
Survey data integration for regression analysis using model calibration
Zhonglei Wang, Hang J. Kim, Jae Kwang Kim
We consider regression analysis in the context of data integration. To combine partial information from external sources, we employ the idea of model calibration which introduces a…
Matrix Completion for Survey Data Prediction with Multivariate Missingness
Xiaojun Mao, Zhonglei Wang, Shu Yang
The National Health and Nutrition Examination Survey (NHANES) studies the nutritional and health status over the whole U.S. population with comprehensive physical examinations and…
Hypotheses Testing from Complex Survey Data Using Bootstrap Weights: A Unified Approach
Jae-kwang Kim, J. N. K. Rao, Zhonglei Wang
Standard statistical methods that do not take proper account of the complexity of survey design can lead to erroneous inferences when applied to survey data due to unequal selectio…
Bootstrap inference for the finite population total under complex sampling designs
Zhonglei Wang, Jae Kwang Kim, Liuhua Peng
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results under complex survey sampling. Most studies about bootstrap-based inference are de…
Sampling techniques for big data analysis in finite population inference
Jae Kwang Kim, Zhonglei Wang
In analyzing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the select…