Efficient and adaptive estimation for semiparametric models - P. J. Bickel; Ch. A. J. Klaassen; Ya 'acov Ritov; J. A. Wellner.

By H. Strasser. 1997

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Citations in the corpus

Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, positive): “The ﬁnite-horizon eﬃciency bounds for ρ π e T under M 1 and M 2 are derived in Kallus and Uehara (2019a) using the uniﬁed semiparametric approach (Bickel et al., 1998).”
Maximum Likelihood Estimation in a Semiparametric Logistic/Proportional‐Hazards Mixture Model (2005, unknown): “,(Yn, dn,Zn) of V 1⁄4 (Y,d,Z). Previous work for the semiparametric logistic/proportional-hazards mixture model has focused on developing point estimation procedures. Kuk & Chen (1992) considered estimation of regression parameters using a marginal likelihood method. Sy & Taylor (2000) and Peng & De”
Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models (2007, unknown): “See Horowitz (1998), Manski (1994), Powell (1994) and Bickel et al. (1993) for examples.”
Non‐parametric tests for distributional treatment effect for randomly censored responses (2009, background): “By the preservation of Hellinger differentiability (see Bickel et al. (1993), page 461) from .”
Double machine learning for treatment and causal parameters (2016, unknown): “See, for example, the important work by [14], [50], [40], [54], [2], [41], [49], [38], [15], [19], [53], and [1].”
Honest confidence regions and optimality in high-dimensional precision matrix estimation (2015, unknown): “This approach in inspired by literature on semiparametric statistics Bickel et al. (1993), van der Vaart (2000).”
Non‐parametric methods for doubly robust estimation of continuous treatment effects (2015, unknown): “a/ is not pathwise differentiable, and root n consistent estimators do not exist (Bickel et al., 1993; Dı́az and van der Laan, 2013).”
Analysis of longitudinal data with irregular, outcome‐dependent follow‐up (2004, unknown): “t/ is infinite dimensional, Newey (1990), chapter 3 of Bickel et al. (1993), chapter 25 of van der Vaart (1998) and sections 1.”
Estimating linear functionals in nonlinear regression with responses missing at random (2009, background): “A reference for the convolution theorem and the characterization is Bickel et al. (1998).”
A Spline‐Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval‐Censored Data (2010, unknown): “…the non-parametric estimator of the baseline cumulative hazard function converges at a rate slower than the standard rate n1/2, the maximum likelihood estimator of regression parameter is still asymptotically normal and achieves the semiparametric efficiency bound defined in Bickel et al. (1993).”
Offline Multi-Action Policy Learning: Generalization and Optimization (2018, background): “…such conditions as well as the difference choices of estimators have been extensively studied in the estimation literature (see for instance Newey (1994), Robins and Rotnitzky (1995), Bickel et al. (1998), Farrell (2015), Belloni et al. (2017), Hirshberg and Wager (2018), Newey and Robins (2018)).”
Robustness, Infinitesimal Neighborhoods, and Moment Restrictions (2009, background): “Also, since Rn (Q, θ) is continuous in (Q, θ) ∈ M × Θ under the Lévy metric and Θ is compact, the maximum theorem (e.g., Berge (1963)) implies that the minimizer T̄ (Q) is upper semi-continuous at each Q ∈ M under the Lévy metric.”
On estimation efficiency of the central mean subspace (2014, unknown): “The general approach that we take is semiparametric and geometric, as illustrated in Bickel et al. (1993) and Tsiatis (2006). We benefit from a simple parameterization that converts the space identification problem into a problem of estimating a finite dimensional parameter in a semiparametric model”