Activity Number:
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435
- Introductory Overview Lecture: Quantile Regression
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Type:
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Invited
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Date/Time:
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Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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JSM Partner Societies
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Abstract #325066
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Title:
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Quantile Regression Analysis of Heterogeneous Data in Ultra-High Dimension
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Author(s):
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Lan Wang*
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Companies:
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University of Minnesota
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Keywords:
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Abstract:
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Modern high-dimensional data are often heterogeneous in the sense that the covariates (predictors) often influence not only the location but also the dispersion or other aspects of the conditional distribution. Quantile regression enjoys some unique advantages for analyzing high dimensional heterogeneous data. By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. The sparsity level is allowed to be different at different quantile levels. We will provide an overview of some recent results in the literature on the statistical theory and algorithms for high-dimensional penalized linear/nonlinear quantile regression.
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Authors who are presenting talks have a * after their name.
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