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 …

arxiv

More from arxiv.org / stat.ML updates on arXiv.org

Data Engineer, Buy with Prime

@ Amazon.com | Santa Monica, California, USA

Data Architect – Public Sector Health Data Architect, WWPS

@ Amazon.com | US, VA, Virtual Location - Virginia

[Job 8224] Data Engineer - Developer Senior

@ CI&T | Brazil

Software Engineer, Machine Learning, Planner/Behavior Prediction

@ Nuro, Inc. | Mountain View, California (HQ)

Lead Data Scientist

@ Inspectorio | Ho Chi Minh City, Ho Chi Minh City, Vietnam - Remote

Data Engineer

@ Craftable | Portugal - Remote