Sharp minimax tests for large Toeplitz covariance matrices with repeated observations

DOI10.1016/j.jmva.2015.09.003zbMath1334.62075arXiv1506.01557OpenAlexW1597752104MaRDI QIDQ268748

Cristina Butucea, Rania Zgheib

Publication date: 15 April 2016

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.01557

zbMATH Keywords

Toeplitz matrixcovariance matrixhigh-dimensional dataminimax hypothesis testingoptimal separation ratessharp asymptotic ratesU-statistic

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Hypothesis testing in multivariate analysis (62H15)

Related Items (5)

An introduction to recent advances in high/infinite dimensional statistics ⋮ Fast nonasymptotic testing and support recovery for large sparse Toeplitz covariance matrices ⋮ Sharp minimax tests for large covariance matrices and adaptation ⋮ Median-of-means approach for repeated measures data ⋮ Adaptive test for large covariance matrices with missing observations

Uses Software

• WeightedPortTest

Cites Work

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