Simultaneous confidence intervals based on the percentile bootstrap approach

Micha Mandel; Rebecca A. Betensky

doi:10.1016/j.csda.2007.07.005

Simultaneous confidence intervals based on the percentile bootstrap approach

Micha Mandel^*, Rebecca A. Betensky

^*Corresponding author for this work

Department of Statistics

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

50 Scopus citations

Abstract

This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O (mB log (B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals.

Original language	English
Pages (from-to)	2158-2165
Number of pages	8
Journal	Computational Statistics and Data Analysis
Volume	52
Issue number	4
DOIs	https://doi.org/10.1016/j.csda.2007.07.005
State	Published - 10 Jan 2008

Bibliographical note

Funding Information:
This research was supported in part by NIH CA075971, the Harvard Center for Neurodegeneration and Repair (HCNR), and the Partners MS Center. We thank Howard Weiner for the permission to use the MS data.

Keywords

Bonferroni
Confidence region
Discrete survival curve
Multiple sclerosis
Normal bound

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10.1016/j.csda.2007.07.005

Cite this

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title = "Simultaneous confidence intervals based on the percentile bootstrap approach",

abstract = "This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O (mB log (B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals.",

keywords = "Bonferroni, Confidence region, Discrete survival curve, Multiple sclerosis, Normal bound",

author = "Micha Mandel and Betensky, {Rebecca A.}",

note = "Funding Information: This research was supported in part by NIH CA075971, the Harvard Center for Neurodegeneration and Repair (HCNR), and the Partners MS Center. We thank Howard Weiner for the permission to use the MS data.",

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AU - Betensky, Rebecca A.

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PY - 2008/1/10

Y1 - 2008/1/10

N2 - This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O (mB log (B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals.

AB - This note concerns the construction of bootstrap simultaneous confidence intervals (SCI) for m parameters. Given B bootstrap samples, we suggest an algorithm with complexity of O (mB log (B)). We apply our algorithm to construct a confidence region for time dependent probabilities of progression in multiple sclerosis and for coefficients in a logistic regression analysis. Alternative normal based simultaneous confidence intervals are presented and compared to the bootstrap intervals.

KW - Bonferroni

KW - Confidence region

KW - Discrete survival curve

KW - Multiple sclerosis

KW - Normal bound

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EP - 2165

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 4

ER -

Simultaneous confidence intervals based on the percentile bootstrap approach

Abstract

Bibliographical note

Keywords

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