Nonparametric bootstrapping for hierarchical data

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

doi:10.1080/02664760903046102

Nonparametric bootstrapping for hierarchical data

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

Research output: Contribution to journal › Article › peer-review

54 Scopus citations

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 language	English (US)
Pages (from-to)	1487-1498
Number of pages	12
Journal	Journal of Applied Statistics
Volume	37
Issue number	9
DOIs	https://doi.org/10.1080/02664760903046102
State	Published - 2010
Externally published	Yes

Keywords

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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10.1080/02664760903046102

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title = "Nonparametric bootstrapping for hierarchical data",

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.",

keywords = "Hierarchical data, Nonparametric bootstrapping, Random effects model, Resampling schemes, Unbalanced data",

author = "Shiquan Ren and Hong Lai and Wenjing Tong and Mostafa Aminzadeh and Xuezhang Hou and Shenghan Lai",

note = "Funding Information: This research was supported by grants from the National Institute on Drug Abuse (DA12777) of United States of America. The authors are grateful to the editor and two referees for comments which improved this paper.",

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T1 - Nonparametric bootstrapping for hierarchical data

AU - Ren, Shiquan

AU - Lai, Hong

AU - Tong, Wenjing

AU - Aminzadeh, Mostafa

AU - Hou, Xuezhang

AU - Lai, Shenghan

N1 - Funding Information: This research was supported by grants from the National Institute on Drug Abuse (DA12777) of United States of America. The authors are grateful to the editor and two referees for comments which improved this paper.

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - Hierarchical data

KW - Nonparametric bootstrapping

KW - Random effects model

KW - Resampling schemes

KW - Unbalanced data

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Nonparametric bootstrapping for hierarchical data

Abstract

Keywords

ASJC Scopus subject areas

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