Add to ORKGIn this paper, we consider an equality test of high-dimensional covariance matrices under the strongly spiked eigenvalue (SSE) model. We introduce an eigenvalue model called the "strongly spiked eigenvalue (SSE) model" which was proposed by Aoshima and Yata (2018). We give a new test procedure based on the spiked eigenstructures
The covariance matrices are essential quantities in econometric and statistical applications includi...
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and ...
This paper analyzes whether standard covariance matrix tests work whendimensionality is large, and i...
We consider the equality test of high-dimensional covariance matrices under the strongly spiked eige...
In this paper, we consider tests of high-dimensional covariance structures under the nonstrongly spi...
In this paper, we consider two-sample tests for covariance matrices in high-dimensional settings. We...
AbstractFor the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081–1102] proposed a s...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
A common feature of high-dimensional data is that the data dimension is high, however, the sample si...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
We consider the five classes of multivariate statistical problems identified by James (1964), which ...
This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample c...
International audienceIn a spiked population model, the population covariance matrix has all its eig...
In this paper, we consider the estimation for the inverse matrix of a high-dimensional covariance ma...
The covariance matrices are essential quantities in econometric and statistical applications includi...
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and ...
This paper analyzes whether standard covariance matrix tests work whendimensionality is large, and i...
We consider the equality test of high-dimensional covariance matrices under the strongly spiked eige...
In this paper, we consider tests of high-dimensional covariance structures under the nonstrongly spi...
In this paper, we consider two-sample tests for covariance matrices in high-dimensional settings. We...
AbstractFor the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081–1102] proposed a s...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
A common feature of high-dimensional data is that the data dimension is high, however, the sample si...
For the test of sphericity, Ledoit and Wolf [Ann. Statist. 30 (2002) 1081-1102] proposed a statistic...
We consider the five classes of multivariate statistical problems identified by James (1964), which ...
This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample c...
International audienceIn a spiked population model, the population covariance matrix has all its eig...
In this paper, we consider the estimation for the inverse matrix of a high-dimensional covariance ma...
The covariance matrices are essential quantities in econometric and statistical applications includi...
This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and ...
This paper analyzes whether standard covariance matrix tests work whendimensionality is large, and i...