We consider, in the setting of p and n large, sample covariance matrices whose population counterparts follow a spiked population model, i.e., with the exception of the first (largest) few, all the population eigenvalues are equal. We study the asymptotic distribution of the partial maximum likelihood ratio statistic and use it to test for the dimension of the population spike subspace. Furthermore, we extend this to the ultra-high-dimensional case, i.e., p>;n. A thorough study of the power of the test gives a correction that allows us to test for the dimension of the population spike subspace even for values of the limit of p/n close to 1, a setting where other approaches have proved to be deficient.Fil: Forzani, Liliana Maria. Consejo Nac...
For random samples of size n obtained from p-variate normal distributions, we consider the classical...
For random samples of size n obtained from p-variate normal distribu-tions, we consider the classica...
We consider the five classes of multivariate statistical problems identified by James (1964), which ...
This paper studies the asymptotic power of tests of sphericity against perturbations in a single unk...
We investigate the likelihood ratio test for a large block-diagonal covariance matrix with an increa...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
AbstractFor the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081–1102] proposed a s...
This paper considers testing a covariance matrix Σ in the high dimensional setting where the dimensi...
We extend a test of subsphericity to the high-dimensional Gaussian regime where the spikes diverge t...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and ...
In this paper we propose a new test procedure for sphericity of the covariance matrix when the dimen...
This article considers testing equality of two population covariance matrices when the data dimensio...
Test statistics for sphericity and identity of the covariance matrix are presented, when the data ar...
The aim of this paper is to establish several deep theoretical properties of principal component ana...
For random samples of size n obtained from p-variate normal distributions, we consider the classical...
For random samples of size n obtained from p-variate normal distribu-tions, we consider the classica...
We consider the five classes of multivariate statistical problems identified by James (1964), which ...
This paper studies the asymptotic power of tests of sphericity against perturbations in a single unk...
We investigate the likelihood ratio test for a large block-diagonal covariance matrix with an increa...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
AbstractFor the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081–1102] proposed a s...
This paper considers testing a covariance matrix Σ in the high dimensional setting where the dimensi...
We extend a test of subsphericity to the high-dimensional Gaussian regime where the spikes diverge t...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and ...
In this paper we propose a new test procedure for sphericity of the covariance matrix when the dimen...
This article considers testing equality of two population covariance matrices when the data dimensio...
Test statistics for sphericity and identity of the covariance matrix are presented, when the data ar...
The aim of this paper is to establish several deep theoretical properties of principal component ana...
For random samples of size n obtained from p-variate normal distributions, we consider the classical...
For random samples of size n obtained from p-variate normal distribu-tions, we consider the classica...
We consider the five classes of multivariate statistical problems identified by James (1964), which ...