Efficient Nonparametric Testing and Estimation

By C. Stein. 1956

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Citations in the corpus

Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning (2019, methodology): “For simplicity and since this question is tangential to our work, similarly to Komunjer and Vuong (2010) and in the spirit of Stein (1956), we simply take Eq.”
The Right Complexity Measure in Locally Private Estimation: It is not the Fisher Information (2018, background): “a local minimax approach, which builds out of the classical statistical literature on hardest onedimensional alternatives that begins with Stein [48, 6, 18, 19, 20, 10, 13].”
On Non‐parametric Testing, the Uniform Behaviour of the t‐test, and Related Problems (2004, methodology): “The idea of using a parametric submodel to obtain efficiency results in non-parametric models dates back to Stein (1956).”
Semiparametric Efficiency Bound for Models of Sequential Moment Restrictions Containing Unknown Functions (2009, unknown): “Our calculation of the bound closely follows the approach of Stein (1956), Newey (1990), Chamberlain (1992a, b) and others (see the appendix for details).”
On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects (1998, methodology): “The semiparametric efficiency bound, introduced by Stein (1956), and developed by Begun, Hall, Huang, and Wellner (1983) and Bickel, Klaassen, Ritov, and Wellner (1993), among others, provides the semiparametric analog of the Cramer-Rao lower bound.”
SEMIPARAMETRIC EFFICIENCY BOUND IN TIME-SERIES MODELS FOR CONDITIONAL QUANTILES (2009, methodology): “We illustrate Stein’s (1956) approach using the example in Section 1.”
Better Bootstrap Confidence Intervals (1987, methodology): “Stein (1956) noted that the least favorable direction Ap transforms in the obvious way under (6.”
Binary Response Models for Panel Data: Identification and Information (2010, methodology): “This idea is owing to Stein (1956) and has been developed by Levit (1975), Begun, Hall, Huang, and Wellner (1983), and Pfanzagl (1982).”
Model Selection in the Presence of Incidental Parameters (2015, unknown): “From White (1982) under Assumptions 1 and 2, it holds that b  =  +  (1) as →∞ even with fixed  .”