Cross-fitting and fast remainder rates for semiparametric estimation

Arbeitspapier

There are many interesting and widely used estimators of a functional with finite semi-parametric variance bound that depend on nonparametric estimators of nuisance func-tions. We use cross-fitting to construct such estimators with fast remainder rates. We give cross-fit doubly robust estimators that use separate subsamples to estimate different nuisance functions. We show that a cross-fit doubly robust spline regression estimator of the expected conditional covariance is semiparametric efficient under minimal conditions. Corresponding estimators of other average linear functionals of a conditional expectation are shown to have the fastest known remainder rates under certain smoothness conditions. The cross-fit plug-in estimator shares some of these properties but has a remainder term that is larger than the cross-fit doubly robust estimator. As specific examples we consider the expected conditional covariance, mean with randomly missing data, and a weighted average derivative.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: cemmap working paper ; No. CWP41/17

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Semiparametric estimation ; semiparametric efficieny ; bias ; smoothness

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Newey, Whitney K. ; Robins, James M.
• Ereignis: Veröffentlichung
• (wer): Centre for Microdata Methods and Practice (cemmap)
• (wo): London
• (wann): 2017

Weitere Informationen

• DOI: doi:10.1920/wp.cem.2017.4117
• Handle: http://hdl.handle.net/10419/189762
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ