Likelihood ratio test for partial sphericity in high and ultra-high dimensions
type: Item
instance of: scholarly article
title: Likelihood ratio test for partial sphericity in high and ultra-high dimensions
zbMATH Open document ID: 1368.62148
author: Liliana Forzani
author: Antonella Gieco
author: Carlos Tolmasky
published in: Journal of Multivariate Analysis
publication date: 2017-08-03T00:00:00Z
Mathematics Subject Classification ID: 62H15
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 6756476
zbMATH Keywords: sample covariance matrix
zbMATH Keywords: spiked population model
zbMATH Keywords: high-dimensional statistics
zbMATH Keywords: principal component analysis
zbMATH Keywords: random matrix theory
MaRDI profile type: Publication
full work available at URL: j.jmva.2017.04.001
OpenAlex ID: W2606203676
cites work: Central limit theorems for eigenvalues in a spiked population model
cites work: Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
cites work: Eigenvalues of large sample covariance matrices of spiked population models
cites work: Q5830931
cites work: Fluctuations of the extreme eigenvalues of finite rank deformations of random matrices
cites work: Q3713374
cites work: A Note on the Sphericity Test
cites work: Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
cites work: On the distribution of the largest eigenvalue in principal components analysis
cites work: Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory
cites work: TESTS OF SIGNIFICANCE FOR THE LATENT ROOTS OF COVARIANCE AND CORRELATION MATRICES
cites work: Significance Test for Sphericity of a Normal $n$-Variate Distribution
cites work: Q3219581
cites work: Testing Hypotheses About the Number of Factors in Large Factor Models
cites work: On determining the number of spikes in a high-dimensional spiked population model
cites work: Estimation of the number of spikes, possibly equal, in the high-dimensional case
cites work: Q5449216
cites work: Modeling by shortest data description
cites work: Estimating the dimension of a model
cites work: Q5438749
cites work: Estimating the number of clusters in a data set via the gap statistic
cites work: Q3813073
DOI: 10.1016/J.JMVA.2017.04.001
description: scientific article; zbMATH DE number 6756476