Nonparametric bootstrapping for hierarchical data

Shiquan Ren, Hong Chen Lai, Wenjing Tong, Mostafa Aminzadeh, Xuezhang Hou, Shenghan Lai

Research output: Contribution to journalArticle

Abstract

Nonparametric bootstrapping for hierarchical data is relatively underdeveloped and not straightforward: certainly it does not make sense to use simple nonparametric resampling, which treats all observations as independent. We have provided some resampling strategies of hierarchical data, proved that the strategy of nonparametric bootstrapping on the highest level (randomly sampling all other levels without replacement within the highest level selected by randomly sampling the highest levels with replacement) is better than that on lower levels, analyzed real data and performed simulation studies.

Original languageEnglish (US)
Pages (from-to)1487-1498
Number of pages12
JournalJournal of Applied Statistics
Volume37
Issue number9
DOIs
StatePublished - 2010

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Hierarchical Data
Bootstrapping
Resampling
Replacement
Simulation Study
Strategy
Sampling

Keywords

  • Hierarchical data
  • Nonparametric bootstrapping
  • Random effects model
  • Resampling schemes
  • Unbalanced data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Nonparametric bootstrapping for hierarchical data. / Ren, Shiquan; Lai, Hong Chen; Tong, Wenjing; Aminzadeh, Mostafa; Hou, Xuezhang; Lai, Shenghan.

In: Journal of Applied Statistics, Vol. 37, No. 9, 2010, p. 1487-1498.

Research output: Contribution to journalArticle

Ren, Shiquan ; Lai, Hong Chen ; Tong, Wenjing ; Aminzadeh, Mostafa ; Hou, Xuezhang ; Lai, Shenghan. / Nonparametric bootstrapping for hierarchical data. In: Journal of Applied Statistics. 2010 ; Vol. 37, No. 9. pp. 1487-1498.
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