The Dependent Wild Bootstrap

By Xiaofeng Shao. 2010

209 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

In Statistics and Probability

Citations in the corpus

A Kernel Test of Goodness of Fit (2016, methodology): “The wild bootstrap technique (Shao, 2010; Leucht & Neumann, 2013; Fromont et al., 2012) addresses both problems.”
A Wild Bootstrap for Degenerate Kernel Tests (2014, methodology): “We show that a method of external randomization known as the wild bootstrap may be applied [22, 28] to simulate from the null distribution.”
A Note on Conditional Versus Joint Unconditional Weak Convergence in Bootstrap Consistency Results (2017, methodology): “Many other resampling schemes are included as well: block bootstraps for time series such as the one of Künsch (1989), (possibly dependent) multiplier (or weighted, wild) bootstraps (see, e.g., Shao, 2010) or the parametric bootstrap (see, e.g., Stute et al., 1993; Genest and Rémillard, 2008).”
An Updated Literature Review of Distance Correlation and Its Applications to Time Series (2017, unknown): “They are computed via the wild bootstrap method (Dehling and Mikosch, 1994; Shao, 2010; Leucht and Neumann, 2013) following the algorithm presented in Fokianos and Pitsillou (2018).”
A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing (2013, unknown): “Examples of its use for parametric inference, nonparametric testing and goodnessof-fit testing can be found in Tsukahara (2005), Rémillard and Scaillet (2009), and Genest et al.”
Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints (2012, methodology): “…of D(p̂) which involves generating resamples for y drawn from the constrained model via iid residual resampling (i.e., conditional on the sample {Xi}), which we denote {Y ∗i ,Xi} (for non-iid data the dependent wild bootstrap residual resampling scheme could be used instead; see Shao (2010)).”
Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension (2015, unknown): “The Annals of Statistics 2015, Vol. 43, No. 3, 1117–1140 DOI: 10.1214/14-AOS1301 © Institute of Mathematical Statistics, 2015”
Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “As in Shao (2010) and Hounyo (2014), the dependence between neighboring observations ˇ y ( l, r ) nm and ˇ y ( l, r ) nm ′ is not only preserved, if m and m ′ belong to a particular block, as typical in block-based resampling.”