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Activity Number:
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289
- Recent Advances in Mathematical Statistics and Probability
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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IMS
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Abstract #318342
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Title:
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Inference for Linear Functional in High-Dimensional Quantile Regression
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Author(s):
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Prabrisha Rakshit* and Zijian Guo
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Companies:
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Rutgers, The State University of New Jersey and Rutgers University
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Keywords:
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Quantile regression;
Stable confidence interval;
High-dimensional;
Bias-corrected
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Abstract:
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It is often desirable to construct stable confidence intervals and robust tests based on the quantile regression. In this paper, considering high-dimensional sparse linear model, we propose a bias-corrected estimator for the conditional quantile of the response variable for any loading vector. We establish asymptotic normality of the proposed estimator and construct a confidence interval for the conditional quantile. The validity of our method does not require sparsity conditions on either the loading vector or the precision matrix of the random design. We demonstrate the proposed method via simulation studies.
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Authors who are presenting talks have a * after their name.
