covariance matrix sample size
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
... group covariance components. The problem of estimating the large covariance matrices has long been known in the literature of multivariate ...of covariance matrices become ill-conditioned (when n is ...
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The analysis of very small samples of repeated measurements II: a modified box correction
... estimated covariance structure from the estimation of the regression parameters leads to an improvement in the small sample behaviour of in- ...estimated covariance structure is not used in the ...
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Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation
... time. Covariance inflation, be- sides reducing the sampling error, can also account for under- estimated model ...2005). Covariance localization removes long-range spurious covariances in the ...
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Estimating the Efficient Portfolio in Non-Radial DEA and DEA-R Models
... different sample, using covariance matrix and non-radial DEA and DEA-R models we show that by increasing the sample size, the frontier of the efficient portfolio gradually becomes close ...
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Testing Independence for a Large Number of High–Dimensional Random Vectors
... the size of the proposed test we first establish the limiting spectral distribu- tion(LSD), ...the sample covariance matrix S under the finite second moment condition on the ...the ...
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Eigenvalue regularized covariance matrix estimators for high dimensional data
... a covariance matrix or its inverse, called the precision matrix, is an important and sometimes inevitable task in data ...sample size. This creates problems for traditional estimator ...
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Limiting spectral distribution of the sample covariance matrix of the windowed array data
... the covariance matrix A slowly varies, hence, the effective window length could not be arbitrary ...finite sample size than other estima- tors which are only guaranteed to converge for fixed M ...
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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 ...the sample size. Second, MANOVA relies on the inverse of a covariance matrix and fails to work when the number of variables exceeds ...
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Testing the Equality Hypothesis on a Cross Covariance Matrix
... of sample covariance matrix. It requires that the sample covariance matrix is non-singular, which is the case with proba- bility one when the sample size is larger ...
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A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty
... of covariance matrix) covariance ...with sample size n = 50 and number of co- variates p = ...of sample covariance matrix are over-dispersed as most of them are ...
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Sample space-time covariance matrix estimation
... the sample size N and the specific ground truth R[τ ] are given for a particular problem, the only way to minimise this perturbation is the judicious selection of the range of lags, |τ | ≤ T , over which ...
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Trading strategies in the Italian interbank market
... variance-covariance matrix that we analyzed, since it is the most suitable to the unevenly structure of the ...variance-covariance matrix using standard PCA and tools borrowed from the ...
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Detection of Outliers in Multivariate Data: A Method Based on Influence Eigen
... transition matrix to test a Markov chain for independence [5] and eigenstructure methods are applied to study the co-linear problem in multivariate linear regression ...
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Asymptotic Comparison of Parameters Estimates of Two-parameter Weibull Distribution
... random sample of size n ≤ 23, the best result is obtained by the Weibull probability plot ...the sample size and the value of parameter ...
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APPROXIMATION TO THE COVARIANCE MATRIX FOR STOCHASTIC POINT KINETICS
... In this work, the square root of the matrix of variances in stochastic point kinetics is analytically deduced using Cholesky decomposition. The system under study is solved numerically using the implicit Milstein ...
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Robust Face Recognition Based on Covariance Matrix
... Curtin Faces dataset: 52 subjects and more than 5000 RGB-D images can be catch through the Microsoft Kinect sensor. Each feature captures images in different poses, illumination, facial expressions and decorative ...
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On the Power of Invariant Tests for Hypotheses on a Covariance Matrix
... the covariance matrix of the disturbances in a regression model is an im- portant problem in econometrics and statistics, a prime example being testing the hypothesis of uncorrelatedness of the ...
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Determination of Minimum Sample Size Requirement for Multiple Linear Regression and Analysis of Covariance Based on Experimental and Non-experimental Studies
... minimum sample size of 500 was required to provide an almost accurate sample estimate for partial eta-squared, regression coefficients and r-squared of a target ...minimum sample size ...
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K-MEANS Clustering with a Covariance Matrix
... There are three parameters , and in the proposed distance metric. Since the objective function (1) is linear in A i , it cannot be optimized with respect to A i . To obtain feasible solution the parameter is ...
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Sparse Matrix Inversion with Scaled Lasso
... ization on the off-diagonal matrix only. This is refereed to as the graphical Lasso (GLasso) due to the connection of the precision matrix to Gaussian Markov graphical models. In this GLasso framework, ...
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