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Activity Number:
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416
- SLDS CSpeed 7
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #318583
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Title:
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Distribution-Free Bootstrap Prediction Intervals After Variable Selection
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Author(s):
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Hasthika Rupasinghe* and Lasanthi Watagoda
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Companies:
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Appalachian State University and Appalachian State University
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Keywords:
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Bootstrap;
Forward Selection;
Lasso;
AIC;
BIC;
Cp
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Abstract:
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We propose two new prediction intervals for linear regression models after variable selection. One of the benefits of the proposed prediction intervals compared to most existing ones is that the distribution of the errors does not need to be known. Asymptotic constancy of the proposed prediction intervals was also examined and shown that the predictions are asymptotically optimal. Simulations were used to illustrate that the new intervals are able to produce better predictions even for sparse models. We compare the new prediction intervals with a few widely used prediction intervals in terms of their achieved coverage and length.
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Authors who are presenting talks have a * after their name.
