Consistent Estimation of the Influence Function of Locally Asymptotically Linear Estimators

By Chris A. J. Klaassen. 1987

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Citations in the corpus

Statistically Efficient Off-Policy Policy Gradients (2020, unknown): “Under certain regularity, the set of inﬂuence functions (minus R ( P )) is equal to the set of pathwise derivatives of R ( · ) at P , and the function φ ∗ is exactly given by that with minimal L 2 norm among this set (Bickel et al., 1993; Klaassen, 1987).”
Locally Robust Semiparametric Estimation (2016, methodology): “Newey and Robins (2017). In addition, regularity conditions for debiased GMM are general and simple relative to those for plug-in GMM.”
The semiparametric Bernstein-von Mises theorem (2010, methodology): “Especially noteworthy in this respect is Klaassen (1987) [27], which formulates an equivalence between efficient semiparametric estimability of smooth parameters and estimability of the influence function when n−1/2-consistency is a given.”
Semiparametric efficiency, distribution-freeness and invariance. (2003, unknown): “1993, Section 3.1; or Klaassen 1987, Theorem 3.1): (LF1) H ( n ) lf ( Ł , j 0 ) deﬁned in (2.6) can be obtained as the (cid”
On Profile Likelihood (2000, background): “For in- stance, in his construction of efficient one-step estimators, Klaassen (1987) required estimators i,,~ for such that Poi,,eO = op(n- l /*) .”
Targeted Maximum Likelihood Learning (2006, unknown): “In particular, the latter presents the general one step estimator based on an estimate of the efficient influence curve: see e.g. Klaassen (1987).”
Minimizing Sensitivity to Model Misspecification (2018, background): “In this case, we strengthen the condition of asymptotic linearity, and require that δ̂ be locally asymptotically linear; see, e.g., Klaassen (1987).”