Text

LAWS : A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing

Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of two-dimensional and three-dimensional images. Supplementary materials for this article are available online.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.117 No.539 September 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1370-1383

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FALSE DISCOVERY RATE
ADJUSTED P-VALUE
COVARIATE-ASSISTED INFERENCE
STRUCTURED MULTIPLE TESTING
DEPENDENT TESTS

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1859379

Pernyataan Tanggungjawab

T. Tony Cai, Wenguang Sun, Yin Xia

Versi lain/terkait

Lampiran Berkas

Komentar