Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting
<|reference_start|>Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting: 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.<|reference_end|>
@article{christensen2024double,
    title={Double Descent: Understanding Linear Model Estimation of Nonidentifiable
  Parameters and a Model for Overfitting},
    author={Ronald Christensen},
    journal={arXiv preprint arXiv:2408.13235},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13235},
    primaryClass={stat.ML cs.LG math.ST stat.TH}
}