In applying statistical methods such as principal component analysis, canonical correlation analysis, and sufficient dimension reduction, we need to determine how many eigenvectors of a random matrix ...

Abstract: This book contains a detailed discussion of the matrix operation, its properties, and its applications in finding the solution of linear equations and determinants. Linear algebra is a ...

This is a preview. Log in through your library . Abstract We consider a multivariate heavy-tailed stochastic volatility model and analyze the large-sample behavior of its sample covariance matrix. We ...

Abstract: Calculating the eigenvalues and eigenvectors of a polynomial matrix has proved to be an important problem in signal processing, in recent years. There exists various iterative algorithms for ...

For a covariance or correlation matrix, the sum of its eigenvalues equals the trace of the matrix, that is, the sum of the variances of the n y variables for a covariance matrix, and n y for a ...