Subsampling

By Arnold J. Stromberg. 2001

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In Statistics and Probability

Citations in the corpus

Forward stability and model path selection (2021, neutral): “On the other hand, in order for subsampling to work asymptotically, we need only to subsample at a rate of o ( n ) and that a standardized version of our statistic converges to a non-degenerate distribution (Politis et al. 1999).”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, background): “…and Stuetzle [2006], Chen and Hall [2003], Friedman and Hall [2007] and others, estimators of this form can exhibit surprising properties even in simple situations; meanwhile, using subsampling rather than bootstrap sampling has been found to avoid several pitfalls [e.g., Politis et al., 1999].”
DeltaGrad: Rapid retraining of machine learning models (2020, methodology): “There are many different techniques based on subsampling (Politis et al., 1999).”
Consistency of Random Forests (2014, background): “Section 5 is devoted to the main proofs, and technical results are postponed to Section 6.”
Gaussian Approximation for High Dimensional Time Series (2015, background): “The latter problem has been extensively studied in the scalar case; see Politis et al. (1999), B¨uhlmann (2002), Lahiri (2003), Alexopoulos and Goldsman (2004), among others.”
Asymptotic Theory for Random Forests (2014, background): “Subsampling has often been found to be more easily amenable to theoretical analysis than bootstrapping [e.g. , 26].”
Out-of-bag estimation of the optimal sample size in bagging (2010, methodology): “Subsampling and m-out-of-n bootstrapping with m < n have been proposed in the statistical literature as alternatives to the standard bootstrap [14, 15, 16, 17, 18].”
Confidence sets for split points in decision trees (2007, background): “Efron’s bootstrap fails for d̂n (as pointed out by Bühlmann and Yu [3], page 940), but the subsampling bootstrap of Politis and Romano [14] still works.”
ESTIMATION IN AN INSTRUMENTAL VARIABLES MODEL WITH TREATMENT EFFECT HETEROGENEITY (2012, background): “Lemma 5 (Lemma 1.3.2., Politis et al., 1999).”
Inference for the identified set in partially identified econometric models (2006, methodology): “1 of Politis et al. (1999) that the conclusions of the theorem continue to hold.”
On the uniform asymptotic validity of subsampling and the bootstrap (2012, background): “These results differ from those usually stated in the literature in that they require the convergence to hold uniformly over P instead of just pointwise over P.”
Text as Data (2017, methodology): “The book by Politis, Romano, and Wolf (1999) gives a comprehensive overview.”
An Updated Literature Review of Distance Correlation and Its Applications to Time Series (2017, positive): “They are computed via the subsampling approach suggested by Zhou (2012, Section 5.1), where the choice of the block size is based on the minimum volatility method proposed by Politis et al. (1999, Section 9.4.2).”
Balanced Control of Generalized Error Rates (2010, neutral): “In this sense, we provide a synthesis of our previous work Romano and Wolf (2007) and the works of Beran (1988a, 1988b).”
ON THE CHOICE OF m IN THE m OUT OF n BOOTSTRAP AND CONFIDENCE BOUNDS FOR EXTREMA (2008, methodology): “Bertail, Politis and Romano (1999) and Politis et al. (1999) estimate α using the m out of n bootstrap, and we applied it to the current problem of extrema (as a preliminary stage, before applying our rule; see Section 5).”