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Activity Number:
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617
- Testing
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Type:
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Contributed
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Date/Time:
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Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract #306850
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Title:
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Inference Without Compatibility
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Author(s):
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Michael Law* and Ya'acov Ritov
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Companies:
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University of Michigan and university of michigan
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Keywords:
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Exponential weights;
Linear models;
Inference;
Compatibility
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Abstract:
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We consider hypothesis testing problems for a single covariate in the context of a linear model with Gaussian design when p>n. Under minimal sparsity conditions of their type and without any compatibility condition, we construct an asymptotically Gaussian estimator with variance equal to the oracle least-squares. The estimator is based on a weighted average of all models of a given sparsity level in the spirit of exponential weighting. We adapt this procedure to estimate the signal strength and provide a few applications. We support our results using numerical simulations based on algorithm which approximates the theoretical estimator and provide a comparison with the de-biased lasso.
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Authors who are presenting talks have a * after their name.
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