Bootstrap Methods: Another Look at the Jackknife

By D. Hinkley. 2008

9,299 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

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Citations in the corpus

Verified Uncertainty Calibration (2019, background): “calibration error (ECE)? Our preliminary experiments suggest that the Bootstrap [46] can be a generic way to debias the ` p calibration error, can we show this? 9”
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation (2016, neutral): “See the work of Efron [6] for a more detailed introduction to bootstrapping.”
SinGAN: Learning a Generative Model From a Single Natural Image (2019, positive): “The variance was estimated by bootstrap [14].”
ExplainaBoard: An Explainable Leaderboard for NLP (2021, methodology): “In practice, we use boot-strapping method (Efron, 1992; Ye et al., 2021) to calculate the conﬁdence interval.”
A semiparametric multivariate and multisite weather generator (2007, methodology): “[5] Classical bootstrap techniques [Efron, 1979; Hardle and Bowman, 1988; Zucchini and Adamson, 1989; Yakowitz, 1993] were developed quite a while ago in the statistics literature for estimating standard errors, and then have been used largely for providing confidence intervals for estimates from…”
Thirty Years of Heteroskedasticity-Robust Inference (2013, methodology): “This is very similar to bootstrap resampling for a single variable as originally proposed in Efron (1979, 1982).”
Maximum Likelihood Estimation of Functionals of Discrete Distributions (2014, methodology): “In particular, the bootstrap [55] as a method for estimating functionals was essentially only analyzed in an asymptotic setting [56].”
Forecasting intermittent inventory demands: simple parametric methods vs. bootstrapping ☆ (2015, methodology): “Alternative bootstrapping methods are found in Efron (1979), Snyder (2002), Willemain, Smart, and Schwarz (2004, hereafter WSS), Porras and Dekker (2008), Teunter and Duncan (2009), Zhou and Viswanathan (2011), and Snyder et al.”
Ultrafast Approximation for Phylogenetic Bootstrap (2013, methodology): “Since the groundbreaking work of Felsenstein (1985), nonparametric bootstrapping (Efron 1979) has become one of the widely used tools to estimate the phylogenetic support of certain clades or splits in an inferred phylogenetic tree.”
Testing Statistical Significance of the Area under a Receiving Operating Characteristics Curve for Repeated Measures Design with Bootstrapping (2005, methodology): “(Efron 1979; Efron, Tibshirani 1986).”
Bootstrap-based maximum multivariate CUSUM control chart (2018, methodology): “Bootstrap is one of the resampling methods that are widely used to estimate the parameter of unknown distribution (Efron, 1979; Efron & Tibshirani, 1994). Bootstrap method can be used to establish the control limits of a control chart whose statistic does not need to meet any assumption of distribut”
Predictive inference with the jackknife+ (2019, methodology): “Early works developing the jackknife and bootstrap methods include Quenouille [18], Quenouille [19], Tukey [26], Miller [15], Efron [8], Stine [23].”
Pollen productivity estimates strongly depend on assumed pollen dispersal (2013, methodology): “Error estimates were calculated by bootstrapping (Efron, 1979) over 1000 model runs.”
Bootstrap Methods in Econometrics (2006, neutral): “The bootstrap was originally proposed as a method for computing standard errors; see Efron (1979, 1982).”
A joint quantile and expected shortfall regression framework (2017, positive): “2) are not differentiable and non-convex for all applicable choices of the specification functions (Fissler, 2017), we apply a derivative-free global optimization technique. More specifically, we use the Iterated Local Search (ILS) meta-heuristic of Lourenço et al. (2003), which successively refines”
Improved estimators for a general class of beta regression models (2008, methodology): “Moreover, the second order biases of µ̂ and φ̂ can be written, following equation (14), respectively, as B(µ̂) = 1 2 T1(2X̃B(β̂) + F ) + 1 2 S1Pββ , and B(φ̂) = T2ZB(θ̂) + 1 2 S2Pθθ, with T1 and T2 as defined in page 4, and, S1 and S2 as defined in page 9.”
Bootstrapped Thompson Sampling and Deep Exploration (2015, methodology): “The term bootstrap refers to a class of methods for nonparametric estimation from data-driven simulation [4].”