Efficient and adaptive estimation for semiparametric models

By P. Bickel, Chris A. J. Klaassen, Yaacov Ritov, Jon A. Wellner. 1998

Citations in the corpus

• The hardness of conditional independence testing and the generalised covariance measure (2018, neutral): “(cid:11) In particular, the requirement of estimating nonparametric quantities (in our case A f and (cid:11) A g ) at a o(n − 1 / 4 ) rate is common for estimators of functionals based on estimating equations involving influence functions [9].”
• 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).”
• Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, neutral): “For a more general and complete presentation, we refer the reader to Bickel et al. (1998); Kosorok (2008); Tsiatis (2006); van der Vaart (1998).”
• Quasi-oracle estimation of heterogeneous treatment effects (2017, neutral): “…literature on semiparametric efficiency and constructions of orthogonal moments including Robinson (1988) and, more broadly, Belloni et al. (2017), Bickel et al. (1998), Newey (1994), Robins (2004), Robins and Rotnitzky (1995), Robins et al. (2017), Tsiatis (2007), van der Laan and Rose (2011),…”
• Semi-parametric modelling in finance: theoretical foundations (2002, unknown): “In section 3 we turn to the framework of elliptical models or of elliptically contoured distributions (Bickel et al 1998, sections 4.2, 6.3, 7.2, 7.8, Fang et al 1990, ch 2, 3, see section 3).”
• Large Vector Auto Regressions (2011, unknown): “This implies that, with probability at least 1 − , there is a local minimizer in the ball {β∗ + αT δ : ‖δ‖ 6 e}, Bickel et al. (1998) and Fan and Li (2001). Consequently, there is a local minimizer of QT (β) such that ‖β̂ − β∗‖ = Op(αT ).”
• Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design (2018, unknown): “There are, however, analogous information inequalities for nonparametric estimation, including Bhattacharyya’s variance bound (Bhattacharyya, 1946), and its generalization due to Bickel (Bickel et al., 1998).”
• Semiparametric Counterfactual Density Estimation (2021, unknown): “A related difference is that the CDF is pathwise differentiable in a nonparametric model, whereas the density function is not [Bickel et al., 1993, van der Laan and Robins, 2003].”
• Policy Learning With Observational Data (2017, unknown): “…by Chernozhukov et al. (2018b) to show asymptotic normality of (11) and, more broadly, that build on assumptions often made in the literature on semiparametric efficiency (Bickel, Klaassen, Ritov, and Wellner, 1998; Chen, Hong, and Tarozzi, 2008; Hahn, 1998; Newey, 1994; Robins and Rotnitzky, 1995).”
• The International Journal of Biostatistics (2017, unknown): “…equation approach that uses the efficient influence curve as an estimating equation (e.g., one-step estimators based on the Newton-Raphson algorithm in Bickel et al. (1997)), and the targeted MLE that uses the efficient influence curve to define a targeted fluctuation function of an initial estimator,…”
• Statistical significance in high-dimensional linear models (2012, unknown): “This idea is pursued with the “efficient score function” approach from semiparametric statistics (Bickel et al. , 1998).”