Haut PDF Conditional covariance estimation for dimension reduction and sensivity analysis
Conditional covariance estimation for dimension reduction and sensivity analysis
... thousands) for the evaluation of the model ...oceanography and others; the scientists have developed Monte-Carlo or quasi Monte-Carlo ...methods. For instance, the Fourier amplitude sensitivity test ...
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Conditional functional principal components analysis
... inequalities and should be dealt with a more precise and care- ful asymptotic study on the effect of presmoothing borrowing for instance ideas from a recent work by Benko et ...Indeed, ...
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Advances in Geometric Statistics for Manifold Dimension Reduction
... Geodesic Analysis (PGA) Instead of an analysis of the covariance matrix, [13] proposed the minimization of squared distances to subspaces which are totally geodesic at a point, a procedure coined ...
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Block-diagonal covariance estimation and application to the Shapley effects in sensitivity analysis
... ). For fixed dimen- sion, we also prove the asymptotic efficiency of this estimator, that performs asymptotically as well as as if the true block-diagonal structure were ...high dimension for ...
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Sequential dimension reduction for learning features of expensive black-box functions
... for learning features of expensive black-box functions ∗ Malek Ben Salem † , Fran¸ cois Bachoc ‡ , Olivier Roustant § , Fabrice Gamboa ‡ , and Lionel Tomaso ¶ ...sensitivity analysis is performed to ...
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Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime
... between covariance matrices is at the core of many machine learning and signal processing ...used for covariance features-based classification (for in- stance in brain signal or ...
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Estimation nonparamétrique de la structure de covariance des processus stochastiques
... Model and definition of the estimator 87 PCA regularization by expanding the empirical eigenvectors in a sparse basis and then apply a thresholding ...methodology for high-dimensional ...
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Anticipative alpha-stable linear processes for time series analysis : conditional dynamics and estimation
... 0, for |z| ≤ 1, (1.22) which is necessary and sufficient to guarantee that the stationary solution (X t ) only depends on «past» values {ε s : s ≤ t} of the ...) and, because of this, are regarded as ...
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Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis
... adjusted for covariates; ...simplistic for many real world ...covariates and the times that passes before some event occurs, including Faraggi-Simon network [4] who proposed a simple feed-forward as ...
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Near-Neighbor Preserving Dimension Reduction for Doubling Subsets of L1
... Science and beyond. Proximity problems in metric spaces of low dimension have been typically handled by methods which discretize the space and therefore are affected by the curse of dimensionality, ...
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A new algorithm for estimating the effective dimension-reduction subspace
... 1], and the errors ε i are ...loss for different values of the sample size n for the first step estimator by SAMM, the final estimator provided by SAMM and the estimator based on ...of ...
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Locally Weighted Full Covariance Gaussian Density Estimation
... Guyon and Vapnik, 1992 ; Vapnik, 1995 ), is due in part to relatively good performance on high dimensional problems, while the traditional kernel methods ...revived, and very promising research trend in ...
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Canopy aerodynamic distance (z-d) estimation and impact on eddy covariance measurements
... – r uw (neutral conditions): pronounced temporal dynamics – r wc and r wT (unstable conditions): no temporal dynamics. – r uw , r wc and r wT : pronounced spatial variability (r uw > r wT > r wc ). 𝑟 ...
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An M-Estimator for Robust Centroid Estimation on the Manifold of Covariance Matrices
... Recently, covariance matrices have been modeled as realiza- tions of Riemannian Gaussian distributions (RGDs) and used in classification algorithms such as k-means or Expectation- Maximization (EM) ...
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Functional estimation of extreme conditional expectiles
... high), and these estimates are then extrapolated using the shape of the conditional heavy-tailed distribution to obtain estimators of properly extreme conditional ...assumptions, and the ...
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Covariance matrix estimation with heterogeneous samples
... the covariance matrix of an observation vector, using heterogeneous training samples, ...whose covariance matrices are not exactly ...same covariance matrix ...sample covariance matrix (SCM) ...
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On automatic bias reduction for extreme expectile estimation
... Intermediate expectiles estimation Y 1 , . . . , Y n are i.i.d. realizations of Y . If α n << 1 − 1/n (or equivalently n(1 − α n ) → ∞ as n → ∞) is an intermediate sequence, two approaches have been ...
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Conditional Propositions and Conditional Assertions
... a conditional assertion account, one needs to recognize that indicative conditionals are conveying information about the speaker’s epistemic ...a conditional assertion) from acceptance conditions ...
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Conditional value-at-risk : aspects of modeling and estimation
... In this section we discuss modeling VaR and related Market Risk measures (MRMs) via conditional quantiles and other techniques.... In practical.[r] ...
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Pépite | Fitting distances and dimension reduction methods with applications
... quality and preference domains study the relation between con- sumers preference and the characteristics of products in order to find the must acceptable characteristics of these products by the consumers ...
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