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Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
cemmap working paper No. CWP20/21
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
We develop two new methods for selecting the penalty parameter for the l1 -penalized high-dimensional M-estimator, which we refer to as the analytic and bootstrap-aftercross-validation methods. For both methods, we derive nonasymptotic error bounds for the corresponding l1 -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
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Dokumentart: 
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