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CLT for linear spectral statistics of large dimensional sample covariance matrices with dependent data
This paper investigates the central limit theorem for linear spectral statistics of high dimensional sample covariance matrices of the form $${\mathbf {B}}_n=n^{-1}\sum _{j=1}^{n}{\mathbf
Large Sample Covariance Matrices and High-Dimensional Data Analysis
1. Introduction 2. Limiting spectral distributions 3. CLT for linear spectral statistics 4. The generalised variance and multiple correlation coefficient 5. The T2-statistic 6. Classification of data
On the convergence of the spectral empirical process of Wigner matrices
It is well known that the spectral distribution Fn of a Wigner matrix converges to Wigner’s semicircle law. We consider the empirical process indexed by a set of functions analytic on an open domain
On the sphericity test with large-dimensional observations
In this paper, we propose corrections to the likelihood ratio test and John's test for sphericity in large-dimensions. New formulas for the limiting parameters in the CLT for linear spectral
Extreme eigenvalues of large-dimensional spiked Fisher matrices with application
Consider two $p$-variate populations, not necessarily Gaussian, with covariance matrices $\Sigma_1$ and $\Sigma_2$, respectively, and let $S_1$ and $S_2$ be the sample covariances matrices from
Self-Excited Threshold Poisson Autoregression
This article studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying
ON ESTIMATION OF THE POPULATION SPECTRAL DISTRIBUTION FROM A HIGH‐DIMENSIONAL SAMPLE COVARIANCE MATRIX
TLDR
A novel solution to the problem of estimating the parametric dimension of the population spectrum when the dimension of a random vector is not negligible with respect to the sample size is proposed, and it is proved that the proposed estimator is strongly consistent and asymptotically Gaussian.
On Recursive Estimation in Incomplete Data Models
TLDR
A new recursive algorithm for parameter estimation from an independent incomplete data sequence, augmented with a Monte-Carlo step which restores the missing data, based on recent results on stochastic algorithms is considered.
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