In this formula, V is the block diagonal matrix of the plot-by-plot correlation structure, with seven copies of R 4 on the diagonal. The matrix Z is the design matrix corresponding to the block effect ...

The covariance matrix of is where -denotes a generalized inverse (refer to Searle 1971). However, G and R are usually unknown and are estimated using one of the aforementioned methods. These estimates ...

More subtle, but significant changes of correlation can also be observed between single stocks and/or between sectors in the stock market. For example, a downward move of the S&P 500 leads to an ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

Covariance matrix estimation, ... PCA has facilitated a more interpretable dimension reduction by adequately modelling structured noise in both the sample and feature spaces, ...

The distribution of the largest latent root of the covariance matrix calculated from a sample from the normal normalitive multivariate population with population covariance matrix σ2 I are presented ...

The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic ...

Zeng Li, Qinwen Wang, Jianfeng Yao, IDENTIFYING THE NUMBER OF FACTORS FROM SINGULAR VALUES OF A LARGE SAMPLE AUTO-COVARIANCE MATRIX, The Annals of Statistics, Vol. 45, No. 1 (February 2017), pp.

This study contributes to the ongoing discussion by investigating whether risk factor disclosures contain valuable information that can be used to improve the estimation of the covariance matrix of ...