Arbeitspapier
Minimax semiparametric learning with approximate sparsity
This paper is about the ability and means to root-n consistently and efficiently estimate linear, mean-square continuous functionals of a high dimensional, approximately sparse regression. Such objects include a wide variety of interesting parameters such as the covariance between two regression residuals, a coefficient of a partially linear model, an average derivative, and the average treatment effect. We give lower bounds on the convergence rate of estimators of such objects and find that these bounds are substantially larger than in a low dimensional, semiparametric setting. We also give automatic debiased machine learners that are 1/Í n consistent and asymptotically efficient under minimal conditions. These estimators use no cross-fitting or a special kind of cross-fitting to attain efficiency with faster than n−1/4 convergence of the regression. This rate condition is substantially weaker than the product of convergence rates of two functions being faster than 1/Í n, as required for many other debiased machine learners.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP32/21
- Klassifikation
-
Wirtschaft
- Thema
-
Approximate sparsity
Lasso
debiased machine learning
linear functional
Riesz representer
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Bradic, Jelena
Chernozhukov, Victor
Newey, Whitney K.
Zhu, Yinchu
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2021
- DOI
-
doi:10.47004/wp.cem.2021.3221
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Bradic, Jelena
- Chernozhukov, Victor
- Newey, Whitney K.
- Zhu, Yinchu
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2021
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