Bootstrapping clustered data

By C. Field, Alan H. Welsh. 2007

376 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20268778b9cfef7211121ea01675c7c14ee45415

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, background): “The available theoretical results are thus quite limited (Field and Welsh, 2007).”
The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence (2014, positive): “Since PK reweight observations within villages, we draw at the village level (Field & Welsh, 2007).”
No time to lose: time constraints and physical activity in the production of health (2010, background): “8 Bootstrap confidence intervals with and without clustering ( Field and Welsh 2007 ) at the state level”
Association of an educational program in mindful communication with burnout, empathy, and attitudes among primary care physicians. (2009, methodology): “Sensitivity analyses used cluster bootstrapped standard errors.(49) Stata SE 10 (StataCorp LP, College Station, Texas), and SAS statistical software version 9.”
Model Selection in Linear Mixed Models (2013, methodology): “We call (3) the transformation model to be consistent with Field and Welsh (2007); it is sometimes called the marginal model (e.g., Jiang (2007)). The difference between the two models is not widely appreciated, but it is important to be clear about which model each procedure is working with. Most m”
Fast and wild: Bootstrap inference in Stata using boottest (2018, background): “…bootstrap would typically 2An alternative large-sample framework assumes that G is small (or fixed) and Ng → ∞ for all g, and combines this with restrictions on the intra-cluster dependence; see, e.g., Hansen (2007). operate by resampling (yg,Xg) pairs at the cluster level (Field and Welsh, 2007).”
Empirical Evaluation of Hierarchical Ground‐Motion Models: Score Uncertainty and Model Weighting (2017, methodology): “When the data within a cluster are also permuted, Field and Welsh (2007) called it randomized cluster bootstrap.”
Inferential Network Analysis with Exponential Random Graph Models (2011, methodology): “This is a recently innovated bootstrapping approach for hierarchically organized data, where observations (e.g. dyads) are independent across units (e.g. years), but not within units (Field and Welsh 2007; Harden Forthcoming).”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, methodology): “The first approach was cluster bootstrapping (Field & Welsh, 2007) which is intended to mimic single-stage cluster sampling and consists of randomly selecting clusters with replacement, and then selecting all the plots within the selected clusters.”