This paper considers the problem of estimating the population spectral distribution from a sample covariance matrix when its dimension is large. We generalize the contour-integral based method in Mestre (2008) and present a local moment estimation procedure. Compared with the original, the new procedure can be applied successfully to models where the asymptotic clusters of sample eigenvalues generated by different population eigenvalues are not all separate. The proposed estimates are proved to be consistent. Numerical results illustrate the implementation of the estimation procedure and demonstrate its efficiency in various cases.postprin
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
In this paper, we improve known results on the convergence rates of spectral distributions of large-...
Abstract. In this paper, we improve known results on the convergence rates of spectral distri-bution...
Abstract: This paper considers the problem of estimating the population spectral distribution from a...
International audienceSample covariance matrices play a central role in numerous popular statistical...
10.1016/j.jspi.2013.06.017Journal of Statistical Planning and Inference143111887-1897JSPI
This paper discusses the problem of estimating the population spectral distri-bution from high-dimen...
This paper discusses the problem of estimating the population spectral distri-bution from high-dimen...
Let (εt)t>0(εt)t>0 be a sequence of independent real random vectors of pp-dimension and let XT=∑s+Tt...
This article studies the limiting behavior of a class of robust population covariance matrix estimat...
AbstractModern random matrix theory indicates that when the population size p is not negligible with...
We consider sample covariance matrices $${S_N=\frac{1}{p}\Sigma_N^{1/2}X_NX_N^* \Sigma_N^{1/2}}$$ wh...
Abstract. We investigate the spectral distribution of large sample covariance matrices with independ...
This thesis presents new results on spectral statistics of different families of large random matric...
International audienceThis paper studies the limiting behavior of a class of robust population covar...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
In this paper, we improve known results on the convergence rates of spectral distributions of large-...
Abstract. In this paper, we improve known results on the convergence rates of spectral distri-bution...
Abstract: This paper considers the problem of estimating the population spectral distribution from a...
International audienceSample covariance matrices play a central role in numerous popular statistical...
10.1016/j.jspi.2013.06.017Journal of Statistical Planning and Inference143111887-1897JSPI
This paper discusses the problem of estimating the population spectral distri-bution from high-dimen...
This paper discusses the problem of estimating the population spectral distri-bution from high-dimen...
Let (εt)t>0(εt)t>0 be a sequence of independent real random vectors of pp-dimension and let XT=∑s+Tt...
This article studies the limiting behavior of a class of robust population covariance matrix estimat...
AbstractModern random matrix theory indicates that when the population size p is not negligible with...
We consider sample covariance matrices $${S_N=\frac{1}{p}\Sigma_N^{1/2}X_NX_N^* \Sigma_N^{1/2}}$$ wh...
Abstract. We investigate the spectral distribution of large sample covariance matrices with independ...
This thesis presents new results on spectral statistics of different families of large random matric...
International audienceThis paper studies the limiting behavior of a class of robust population covar...
Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally sh...
In this paper, we improve known results on the convergence rates of spectral distributions of large-...
Abstract. In this paper, we improve known results on the convergence rates of spectral distri-bution...