Arbeitspapier

Analytic and bootstrap-after-cross-validation methods for selecting penalty parameters of high-dimensional M-estimators

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We develop two new methods for selecting the penalty parameter for the e1-penalized high-dimensional M-estimator, which we refer to as the analytic and bootstrap-after-cross-validation methods. For both methods, we derive nonasymptotic error bounds for the corresponding e1-penalized M-estimator and show that the bounds converge to zero under mild conditions, thus providing a theoretical justification for these methods. We demonstrate via simulations that the finite-sample performance of our methods is much better than that of previously available and theoretically justified methods.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP03/22

Classification
Wirtschaft
Subject
Penalty parameter selection
penalized M-estimation
high-dimensional models
sparsity
cross-validation
bootstrap

Event
Geistige Schöpfung
(who)
Chetverikov, Denis N.
Sørensen, Jesper R.-V.
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2022

DOI
doi:10.47004/wp.cem.2022.0322
Handle
Last update
10.03.2025, 11:44 AM CET

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Object type

  • Arbeitspapier

Associated

  • Chetverikov, Denis N.
  • Sørensen, Jesper R.-V.
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2022

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