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Activity Number:
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466
- First-Hitting-Time Based Threshold Regression and Applications
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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Abstract #328920
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Title:
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Censored Threshold Regression with Diverging Number of Covariates
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Author(s):
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Takumi Saegusa* and Mei-Ling Ting Lee
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Companies:
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University of Maryland and University of Maryland
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Keywords:
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survival;
threshold regression;
variable selection
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Abstract:
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We develop a new sparse threshold regression method for high-dimensional survival data with right censoring. Our method is an iteratively reweighted penalized threshold regression where the number of covariates diverges as a sample size goes to infinity. We establish its oracle property for variable selection and estimation errors, a grouping property for highly correlated covariates, and asymptotic normality of the proposed estimator. The performance of our method is illustrated using simulations and real data examples.
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Authors who are presenting talks have a * after their name.
