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Autore principale: Christensen, Ronald
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2408.13235
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author Christensen, Ronald
author_facet Christensen, Ronald
contents We consider ordinary least squares estimation and variations on least squares estimation such as penalized (regularized) least squares and spectral shrinkage estimates for problems with p > n and associated problems with prediction of new observations. After the introduction of Section 1, Section 2 examines a number of commonly used estimators for p > n. Section 3 introduces prediction with p > n. Section 4 introduces notational changes to facilitate discussion of overfitting and Section 5 illustrates the phenomenon of double descent. We conclude with some final comments.
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id arxiv_https___arxiv_org_abs_2408_13235
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting
Christensen, Ronald
Machine Learning
Statistics Theory
We consider ordinary least squares estimation and variations on least squares estimation such as penalized (regularized) least squares and spectral shrinkage estimates for problems with p > n and associated problems with prediction of new observations. After the introduction of Section 1, Section 2 examines a number of commonly used estimators for p > n. Section 3 introduces prediction with p > n. Section 4 introduces notational changes to facilitate discussion of overfitting and Section 5 illustrates the phenomenon of double descent. We conclude with some final comments.
title Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting
topic Machine Learning
Statistics Theory
url https://arxiv.org/abs/2408.13235