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Yunxiao Chen; Chengcheng Li; Jing Ouyang; Gongjun Xu – Grantee Submission, 2023
We consider the statistical inference for noisy incomplete binary (or 1-bit) matrix. Despite the importance of uncertainty quantification to matrix completion, most of the categorical matrix completion literature focuses on point estimation and prediction. This paper moves one step further toward the statistical inference for binary matrix…
Descriptors: Statistical Inference, Matrices, Voting, Federal Government
Gongjun Xu; Tony Sit; Lan Wang; Chiung-Yu Huang – Grantee Submission, 2017
Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to incorrect inference. We propose a unified estimation procedure and a computationally fast resampling method to make statistical inference for…
Descriptors: Sampling, Statistical Inference, Computation, Generalization