Web: http://arxiv.org/abs/2201.10208

Jan. 26, 2022, 2:10 a.m. | Abhishek Chakrabortty, Guorong Dai, Raymond J. Carroll

stat.ML updates on arXiv.org arxiv.org

We consider quantile estimation in a semi-supervised setting, characterized
by two available data sets: (i) a small or moderate sized labeled data set
containing observations for a response and a set of possibly high dimensional
covariates, and (ii) a much larger unlabeled data set where only the covariates
are observed. We propose a family of semi-supervised estimators for the
response quantile(s) based on the two data sets, to improve the estimation
accuracy compared to the supervised estimator, i.e., the sample …


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