# Haut PDF Conditional covariance estimation for dimension reduction and sensivity analysis

### Conditional covariance estimation for dimension reduction and sensivity analysis

**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

**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

**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

**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

**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

**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

**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

**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

**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

**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

**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

**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) ...

6

### Functional estimation of extreme conditional expectiles

**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

**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

**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

**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

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### Pépite | Fitting distances and dimension reduction methods with applications

**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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