Bootstrap Sample Size in Nonregular Cases

By Jianwen Shao. 1994

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Citations in the corpus

Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “It is known (Shao 1994) that commonly employed results like the consistency of the bootstrap distribution and the Central Limit Theorem do not hold in cases where the true parameters—in this case the true Q-values—are at or near these points of non-differentiability.”
Regression on manifolds: Estimation of the exterior derivative (2011, background): “The distinguishing feature of our estimators is the particular form of the regularizing matrix used.”
Incorrect asymptotic size of subsampling procedures based on post-consistent model selection estimators (2009, methodology): “Using the m out of n bootstrap for a post-model selection estimator has been suggested by Shao (1994, 1996).”
Nonparametric variable importance assessment using machine learning techniques (2020, background): “However, this is not advisable since, besides the computational burden of such an approach, there is little theory to justify using the standard nonparametric bootstrap in this context, particularly for the naive ANOVA-based estimator (Shao, 1994).”
ASYMPTOTIC SIZE AND A PROBLEM WITH SUBSAMPLING AND WITH THE m OUT OF n BOOTSTRAP (2009, unknown): “…1983; Swanepoel, 1986; Athreya, 1987; Beran and Srivastava, 1987; Shao and Wu, 1989; Wu, 1990; Eaton and Tyler, 1991; Politis and Romano, 1994; Shao, 1994, 1996; Beran, 1997; Bickel, Götze, and van Zwet, 1997; Andrews, 1999, 2000; Politis, Romano, and Wolf, 1999 (hereafter PRW); Romano and…”
Inference on Directionally Differentiable Functions (2014, methodology): “…numerical estimator is implicit of Dümbgen (1993), which is numerically identical to composing φ s n , n with a non-parametric G the of n bootstrap of Shao (1994) is numerically equivalent to setting s = 1 / r and composing ˆ φ (cid:5) with the m out of n bootstrap estimator of the distribution of…”