Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/260384 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
cemmap working paper No. CWP03/22
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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.
Schlagwörter: 
Penalty parameter selection
penalized M-estimation
high-dimensional models
sparsity
cross-validation
bootstrap
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
1.31 MB





Publikationen in EconStor sind urheberrechtlich geschützt.