Sample Covariance
Asymptotic distribution of sample covariance determinant
... Abstract Under normality, an asymptotic distribution of sample covariance determi- nant will be derived. We show that this asymptotic distribution is more applicable in practice than the classical one. This ...
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Limiting spectral distribution of the sample covariance matrix of the windowed array data
... Remark 1. In the economic literature, other methods have been proposed to approximate the spectral density function of exponentially weighted financial covariance matrices for Portfolio Optimization [34,35]. These ...
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Robust estimation of biometric data
... Then from tables 1 to 18, we can clearly see that the MCD-estimator performs best under progressively worse outliers. Even for R = 30, the average covari- ance matrix did almost not differ from the previous two averages. ...
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The analysis of very small samples of repeated measurements II: a modified box correction
... (OLS) covariance estimate is in keeping with the spirit of this ...complex covariance structure that the data will support, such as a (high order) antede- pendence ...the covariance structure, so ...
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Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation
... background-error covariance matrix can be estimated from the ...background-error covariance matrix is sub- ject to the so-called sampling ...background-error covariance matrix can be improved by ...
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Testing Independence for a Large Number of High–Dimensional Random Vectors
... the sample covariance matrix for n covariance stationary time series and the CLT of the linear spectral ...finite sample performance of the test and investigate some kinds of commonly used ...
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Spectral diagonal ensemble Kalman filters
... the sample covariance in the analysis step by its diagonal in a spectral ...the covariance when the covariance itself is diagonal in the spectral basis, as is the case, ...
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Contributions to high dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand
... the sample covariance in high-dimensional problems is well-known to be ...the sample covariance in a high-dimensional ...the sample covariance matrix, the ridge regular- ization, ...
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High dimensional Global Minimum Variance Portfolio
... different covariance estimators—the spectral corrected covariance, the sample covariance, the linear shrinkage covariance, the nonlinear shrink- age ...population covariance ...
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Martingale approximation for common factor representation
... In this paper a martingale approximation is used to derive the limiting distribution of sim- ple positive eigenvalues of the sample covariance matrix for a stationary linear process. The derived ...
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Contributions to high dimensional data analysis : some applications of the regularized covariance matrices : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand
... The sample covariance matrix becomes rank deficient and is not invertible when the number of variables are more than the number of ...a covariance matrix. Recently, regularized alternatives to the ...
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A Survey of Various Spectrum Sensing Techniques in Cognitive Radio Networks: Non Cooperative Systems
... the covariance matrix of received ...the sample covariance matrix other than the statistic covariance ...the sample covariance matrix, we propose two detection methods as ...
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Covariance-based Clustering in Multivariate and Functional Data Analysis
... of covariance operators, which we assumed to be the natural Hilbert-Schmidt norm, and seeks for the partition of data producing the highest possible distance among estimated ...of covariance operators, ...
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Dynamic covariance models
... the sample covariance estimate as defined in (2), and plot selected entries of this matrix in Figure ...two covariance matrices, one for the first 86 scans and the other for the last 86 scans, is ...
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Detection of Outliers in Multivariate Data: A Method Based on Influence Eigen
... an outlier on statistics such as jth eigenvalues, λj or eigenvectors, vj of a sample covariance matrix is simply the change in λj or vj when the ith observation is deleted from the sampl[r] ...
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Continuum direction vectors in high dimensional low sample size data
... the sample size n relatively ...the sample covariance matrix is consistent to its theoretical ...population covariance matrix is extremely large com- pared to the rest of ...population ...
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ESTIMATION OF GENETIC COVARIANCE FROM JOINT OFFSPRING-PARENT AND SIB-SIB STATISTICS
... For all experimental circumstances illustrated in Table 4, large sample approximations considerably underestimate the exact values of the covariance (E,) between the offsprin[r] ...
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Analysis error covariance versus posterior covariance in variational data assimilation
... error covariance matrix (see, for example, Thacker, 1989; Rabier and Courtier, 1992; Fisher and Courtier, 1995; Yang et ...posterior covariance is not exactly the analysis error ...error covariance ...
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A Robust Capon Beamformer against Uncertainty of Nominal Steering Vector
... In the next experiment, the power estimates of the signals at di ff erent directions are evaluated when the ar- ray has arbitrary ASV error. The covariance matrix is es- timated from 100 snapshots. The direction ...
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Analysis of covariance
... of Covariance (ANCOVA) is a statistical method which is an extension of ANOVA that provides a way of statistically controlling the linear effect of variables one does not want to examine in a ...
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