Efficient and Adaptive Estimation for Semiparametric Models.

By K. Do, Peter J. Bickel, Chris A. J. Klaassen, Yaacov Ritov, Jon A. Wellner. 1994

Subfields: Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

The hardness of conditional independence testing and the generalised covariance measure (2018, background): “In particular, the requirement of estimating nonparametric quantities (in our case √ Af and √ Ag) at a o(n−1/4) rate is common for estimators of functionals based on estimating equations involving influence functions [9].”
Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, methodology): “The cone invertibility factor is a generalization of the restricted eigenvalue condition of Bickel et al. (2009), proposed by Ye and Zhang (2010). The concept of the effective dimension does not split t0 into a sparse component and a small dense component, as is done in the now standard analysis of ”
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, methodology): “2 Efficiency Bounds in Infinite Horizons The finite-horizon efficiency bounds for ρπ e T under M1 and M2 are derived in Kallus and Uehara (2019a) using the unified semiparametric approach (Bickel et al., 1998). We re-derive these efficiency bounds from the more primitive approach of Stein (1956), wh”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, neutral): “2, for a complete and rigorous treatment, refer to Bickel et al. (1998); van der Laan and Robins (2003); van der Vaart (2002).”
Efficient Policy Learning (2017, background): “Given unconfoundedness (3), there is a large literature that develops a semiparametric efficient estimation theory for statistics like Q(π) (Bickel et al., 1998; Hahn, 1998; Hirano et al., 2003; Robins and Rotnitzky, 1995; Robins et al., 1995), for any fixed policy π.”
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information (2018, background): “We describe this precisely at the end of this section, first reviewing some of the necessary parametric and semi-parametric theory [52, 5].”
Quasi-oracle estimation of heterogeneous treatment effects (2017, methodology): “…the 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),…”
The Impact of Machine Learning on Economics (2018, background): “From the 1990s through the 2000s, a literature emerged about using semi-parametric methods to estimate average treatment effects (e.g. Bickel et al. (1993), focusing on an environment with a fixed number of covariates that is small relative to the sample size.”
Nonparametric variable importance assessment using machine learning techniques (2020, methodology): “Specifically, we have that (4) is pathwise differentiable with respect to the unrestricted model (see, eg, Bickel et al. , 1998).”
Non‐parametric tests for distributional treatment effect for randomly censored responses (2009, unknown): “…Yν/, p̄ν,fν .q, x, y/≡qfν.y|x/ Ḡν.y|x/πν.x/+ .1−q/gν.y|x/F̄ .y|x/πν.x/: It follows from condition (1) that∫ ∫ ∫ [N1=2ν {pν,fν .c, x, y/1=2 −pν,f .c, x, y/1=2}− 12 tν.x, y/pν,f .c, x, y/1=2]2 dc dy dx→0: By the preservation of Hellinger differentiability (see Bickel et al. (1993), page 461) from .”