Nonparametric bootstrapping for hierarchical data

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

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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
Issue number9
StatePublished - 2010
Externally publishedYes


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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