Correlation matrix

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  • The EViews workfile is defined as a file in EViews, which provides many convenient visual ways, such as (i) to enter and save data sets, (ii) to create new series or variables from existing ones, (iii) to display and print series and (iv) to carry out and save results of statistical analysis, aswell as each equation of the models applied in the analysis.By using EViews, each statistical model that applied previously could be recalled and modified easily and quickly to obtain the best fit model, based on personal judgment using an interactive process.

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  • Over the last two decades there has been a notable increase in the number of corporate governance codes and principles, as well as a range of improvements in structures and mechanisms. Despite this, corporate governance failed to prevent a widespread default of fiduciary duties of corporate boards and managerial responsibilities in the finance industry, which contributed to the 2007–2010 global financial crisis.

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  • We study unitary random matrix ensembles of the form −1 Zn,N | det M |2α e−N Tr V (M ) dM, where α −1/2 and V is such that the limiting mean eigenvalue density for n, N → ∞ and n/N → 1 vanishes quadratically at the origin. In order to compute the double scaling limits of the eigenvalue correlation kernel near the origin, we use the Deift/Zhou steepest descent method applied to the Riemann-Hilbert problem for orthogonal polynomials on the real line with respect to the weight |x|2α e−N V (x) . ...

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  • Most high resolution direction-of-arrival (DOA) estimation methods rely on subspace or eigenbased information which can be obtained fromthe eigenvalue decomposition (EVD) of an estimated correlation matrix, or from the singular value decomposition (SVD) of the corresponding data matrix.

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  • Most high resolution direction-of-arrival (DOA) estimation methods rely on subspace or eigenbased information which can be obtained fromthe eigenvalue decomposition (EVD) of an estimated correlation matrix, or from the singular value decomposition (SVD) of the corresponding data matrix. However, the expense of directly computing these decompositions is usually prohibitive for real-time processing. Also, because theDOAangles are typically time-varying, repeatedcomputation is necessarytotracktheangles.

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  • The SMI algorithm, which is also known as direct matrix inversion (DMI) algorithm, has recently been used for 3G systems and beyond, because the fast convergence property makes it suitable for use with high data rate transmissions [143,150]. However the complexity grows three-orders exponentially with the number of the weights (M 3). Recursive equations for the inverse of the correlation matrix thus had been used for the implementation on digital signal processors.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Matrix-Variate Probabilistic Model for Canonical Correlation Analysis

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  • Beginning with Titman and Wessels (1988), then Rajan and Zingales (1995) and more recently Frank and Goyal (2004), the empirical corporate finance literature has converged to a limited set of variables that are reliably related to the leverage of non-financial firms. Leverage is positively correlated with size and collateral, and is negatively correlated with profits, market-to-book ratio and dividends.

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  • Despite evidence to the contrary, chemical industry critics of epidemiologic studies linking pesticides to major diseases argue that they are of limited value because of their reliance on records and study participants’ memory, among other issues. In fact, the correlation of patterns of chemical use with an effect is difficult to establish in epidemiology and therefore may underestimate hazard effects. When a correlation is established it raises serious concern.

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  • Hopke and co-workers (6), Heidam (7), Henry (8), and Barrie and Barrie (9) applied principal component analysis (PCA) to source identification, but Paatero and Tapper (10, 11) showed that PCA cannot provide a trueminimal variance solution since they are based on an incorrect weighting. In view of the limitations of PCA, a new technique, positive matrix factorization (PMF), was developed for sources identification and apportionment (12).

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  • ICA by Tensorial Methods One approach for estimation of independent component analysis (ICA) consists of using higher-order cumulant tensor. Tensors can be considered as generalization of matrices, or linear operators. Cumulant tensors are then generalizations of the covariance matrix. The covariance matrix is the second-order cumulant tensor, and the fourth order tensor is defined by the fourth-order cumulants cum(xi xj xk xl ). For an introduction to cumulants, see Section 2.7.

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  • Muirhead (1987) reviews the large literature on shrinkage estimators of the covariance matrix in finite-sample statistical decision theory. All these estimators suffer from at least two severe drawbacks, either of which is enough to make them ill-suited to stock returns: (i) they break down when N T; (ii) they do not exploit the a priori knowledge that stock returns tend to be positively correlated to one another.

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  • We report on an investigation of the pragmatic category of topic in Danish dialog and its correlation to surface features of NPs. Using a corpus of 444 utterances, we trained a decision tree system on 16 features. The system achieved nearhuman performance with success rates of 84–89% and F 1 -scores of 0.63–0.72 in 10fold cross validation tests (human performance: 89% and 0.78). The most important features turned out to be preverbal position, definiteness, pronominalisation, and non-subordination. We discovered that NPs in epistemic matrix clauses (e.g. “I think . . .

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  • Heparanase is an endo-b-D-glucuronidase capable of cleaving heparan sul-fate side chains at a limited number of sites, yielding heparan sulfate frag-ments of still appreciable size. Importantly, heparanase activity correlates with the metastatic potential of tumor-derived cells, attributed to enhanced cell dissemination as a consequence of heparan sulfate cleavage and remod-eling of the extracellular matrix and basement membrane underlying epithe-lial and endothelial cells.

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  • Topic 3 - Computing portfolio risk and return. After you have mastered the material in this chapter, you will be able to: Compute correlation and covariance matrices, compute the standard deviation of a portfolio of risky assets, use matrix algebra to compute portfolio return and risk, use VBA comments and application object.

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