The covariance matrix
"Estimating the Covariance Matrix: A New Approach"
... the covariance matrix and the generalized variance when the observations follow a nonsingular multivariate normal dis- tribution with unknown ...mean matrix and dominates the James-Stein minimax ...
22
APPROXIMATION TO THE COVARIANCE MATRIX FOR STOCHASTIC POINT KINETICS
... a matrix formulation consisting of the product of the square root of the variance matrix and a vector of independent Brownian ...same covariance matrix but using the EM (Euler- Maruyama) ...
8
A note on testing the covariance matrix for large dimension
... O. Ledoit and M. Wolf (2002). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Ann. Statist., 30 (4): 1081-1102. E. Liebscher (1996). Central limit ...
10
with t-centre approach to estimate the covariance matrix
... 4 Conclusion Using the traditional ml divergence measure to estimate the model param- eters under multivariate normality assumptions does not deliver a good fit when there is contamination in the data. This ml measure ...
16
"Estimating the Covariance Matrix: A New Approach", June 1999.
... the covariance matrix and the generalized variance when the observations follow a nonsingular multivariate normal dis- tribution with unknown ...mean matrix and dominates the James-Stein minimax ...
26
October 3rd, Linear Algebra & Properties of the Covariance Matrix
... A major part of Markowitz theory was the assumption for C to be a covariance matrix it must be symmetric positive definite (SPD).... Real Matrices/Vectors.[r] ...
27
Robust Face Recognition Based on Covariance Matrix
... θ 1 ≤ θ 2 ≤... θ d ≤ π /2 defined as the minimum angle between any vector in the subspace and any vector in the second subspace. The method of references to estimate 3D image face posture was used, and according to the ...
7
Testing the Equality Hypothesis on a Cross Covariance Matrix
... Xiaomei Chen, Shibin Zhang Department of Mathematics, Shanghai Maritime University, Shanghai, China Abstract This paper is concerned about testing whether a cross-covariance matrix de- viates from a ...
14
Honey, I Shrunk the Sample Covariance Matrix
... B Formula for Shrinkage Intensity We have to choose the objective according to which the shrinkage intensity δ is optimal. All existing shrinkage estimators from finite-sample statistical decision theory and also the one ...
22
A Kernel Technique for Forecasting the Variance-Covariance Matrix
... Bull and Bear Market Phases 100% 100% 85.7% Table 3: Variables Included in the Kernel Model. 9 Conclusion This paper presents a ‡exible kernel model which can be used to forecast symmetric, positive de…nite variance ...
39
Honey, I shrunk the sample covariance matrix
... Here g is the manager’s target gain (i.e., expected excess return) relative to the bench- mark. A typical number is 300 basis points (annualized). The manager chooses g and the upper limit c and also knows the current ...
21
Adaptive Thresholding for Sparse Covariance Matrix Estimation
... sparse covariance matrix estimation is intrinsically a heteroscedastic problem in the sense that the entries of the sample covariance matrix could have a wide range of ...the covariance ...
38
Covariance Matrix Filtering for Adaptive Beamforming with Moving Interference
... Much remains to be done to develop these methods further. There is great potential for refinement of the algorithms and development of better filtering techniques. The epoch frequency domain has only begun to be ...
7
Cosmic shear covariance matrix in wCDM: Cosmology matters
... lensing covariance directly from numerical simulations, reproducing remarkably well the Fisher results from the analytical models at most ...the covariance matrix is evaluated in the first place ...
27
Evaluating dynamic covariance matrix forecasting and portfolio optimization
... and covariance matrix of the bulk of the data and let ⌊ ⌋ be the operator that takes the integer part of its ...and covariance matrix of the subset size ⌊ ⌋ for which the determinant of the ...
82
Evaluating covariance matrix forecasts in a value-at-risk framework
... the covariance matrix specifications under the t(13) and generalized-t distributions; that is, EWMA-normal perform better than these alternatives greater than 75% of the ...
52
The performance of robust heteroscedasticity consistent covariance matrix estimator
... the covariance matrix becomes in- ...OLS covariance ma- trix with heteroscedasticity consistent covariance matrix (HCCM) estimator denoted by ...
18
Gini Covariance Matrix and its Affine Equivariant Version
... Their properties have been explored and their influence functions have been derived. It was found that the influence functions of GCM are approximately linear and, therefore, is unbounded. In a strict sense, they are not ...
116
Sparsistency and rates of convergence in large covariance matrix estimation
... sparse covariance and precision matrices based on penalized likelihood with non-concave penalty ...the covariance matrix, its inverse or its Cholesky ...
35
K-MEANS Clustering with a Covariance Matrix
... on covariance matrix is ...inducing matrix so that the clusters with dissimilar structres that inherent in a data set can be ...singularity matrix or by considering linearly correlated or ...
8