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Activity Number:
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471
- Advances in High-Dimensional Inference and Multiple Testing
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #301736
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Title:
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Testing High-Dimensional Null Hypothesis Against High-Dimensional Alternative for Generalized Linear Models
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Author(s):
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Jinsong Chen* and Hua Yun Chen
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Companies:
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University of Illinois at Chicago and University of Illinois at Chicago
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Keywords:
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hypothesis testing;
high dimension
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Abstract:
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We are interested in testing the regression parameters in high-dimensional generalized linear models. Since the number of parameters is high-dimensional under both null and alternative hypotheses, the test statistics is developed from desparsified post lasso estimates for free parameters under null hypothesis. We build the asymptotic normal distribution of test statistics under null, and examine asymptotically the power properties of the tests under alternative. Intensive simulation studies are conducted to assess the performance of our method.
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Authors who are presenting talks have a * after their name.
