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Keywords: Markowitz model, sensitivity analysis, covariance matrix estimation, return vector ? and the covariance matrix Q between the returns over the 11 Jan 2018 We consider global sensitivity analysis for a function f(·) depending on d real .. correlation matrix, whereas only its n diagonal terms need to be used for the tutorial. Statistical Science, 14(4):382 417, 1999. [19] J.P. Imhof. 19 Jan 2016 Principal component analysis (PCA) is a technique for reducing the dimensionality of .. (i) Covariance and correlation matrix principal component analysis .. By its very nature, PCA is sensitive to the presence of outliers and 7 Apr 2010 This paper de- scribes an estimator-independent method based on sensitivity analysis that al- lows computing the residual covariance matrix. 6 Feb 2008 Global sensitivity analysis of complex numerical models can be performed by calculat- .. The mean and covariance matrix of this vector are computed using those of .. Bayesian analysis of computer code outputs: A tutorial. 29 Aug 2013 Principal component analysis (PCA) is a statistical procedure that uses an orthogonal PCA is sensitive to the relative scaling of the original variables. . Then, we compute the covariance matrix of the data, and calculate the eigenvalues and corresponding Jonathon Shlens, A Tutorial on Principal Component Analysis. Variance-based sensitivity analysis is a form of global sensitivity analysis. Working within a .. Generate an N?2d sample matrix, i.e. each row is a sample point in the hyperspace of 2d dimensions. This should be done with respect to the T2 1. QUADOS. Nuclear Data Sensitivity and Uncertainty. Analysis. I. Kodeli Tutorial Lecture 2 Cross section covariance matrices (construction, available. freedom of a given estimation problem (Correlation, Rank of sensitivity matrix, SVD, . 3.3.3.6 Residuals analysis and signature of the presence of a bias in the.