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High-Dimensional MANOVA Via Bootstrapping and Its Application to Functional and Sparse Count Data

We propose a new approach to the problem of high-dimensional multivariate ANOVA via bootstrapping max statistics that involve the differences of sample mean vectors. The proposed method proceeds via the construction of simultaneous confidence regions for the differences of population mean vectors. It is suited to simultaneously test the equality of several pairs of mean vectors of potentially more than two populations. By exploiting the variance decay property that is a natural feature in relevant applications, we are able to provide dimension-free and nearly parametric convergence rates for Gaussian approximation, bootstrap approximation, and the size of the test. We demonstrate the proposed approach with ANOVA problems for functional data and sparse count data. The proposed methodology is shown to work well in simulations and several real data applications.

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Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.118 No.541 March 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 177-191

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

HYPOTHESIS TESTING
FUNCTIONAL DATA ANALYSIS
PHYSICAL ACTIVITY
POISSON DATA
GAUSSIAN APPROXIMATION
BOOTSTRAP METHODS
MEAN FUNCTION
SIMULTANEOUS CONFIDENCE INTERVALS

Info Detail Spesifik

https://doi.org/10.1080/01621459.2021.1920959

Pernyataan Tanggungjawab

Zhenhua Lin, Miles E. Lopes, Hans-Georg Muller

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