[PDF] Top 20 Non linear robust regression in high dimension
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Non linear robust regression in high dimension
... Horaud. High-dimensional regression with Gaus- sian mixtures and partially-latent response ...Inverse regression approach to robust non-linear high-to-low dimensional ... Voir le document complet
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Identification-robust inference for endogeneity parameters in linear structural models
... ond, the simultaneity covariance (or regression) coefficients determine the estimation bias of least- squares methods. Information on the size of such biases can be useful in interpreting least-squares ... Voir le document complet
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VARIABLE SELECTION AND ESTIMATION IN MULTIVARIATE FUNCTIONAL LINEAR REGRESSION VIA THE LASSO
... FUNCTIONAL LINEAR REGRESSION VIA THE LASSO ANGELINA ROCHE Abstract. In more and more applications, a quantity of interest may depend on several covariates, with at least one of them ... Voir le document complet
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High-dimension multi-label problems: convex or non convex relaxation?
... of non linear multi-label problems for image processing ...and non-convex relaxations of the non linear problems and we have shown that computing the global minima of a relaxed energy ... Voir le document complet
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Higher Moment Estimators for Linear Regression Models With Errors in the Variables
... shown in the appendix of the MRW paper, we have accurately reproduced in our Table 6, the results appearing in the upper part of Table 2 of ...errors in the ...powerful in small samples ... Voir le document complet
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Gaussian process regression with linear inequality constraints
... process regression is one of the most popular ...etc. In this paper, we introduce a framework for incorporating any type of linear constraints in Gaussian process modeling, including common ... Voir le document complet
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Variable selection and estimation in multivariate functional linear regression via the Lasso
... the dimension of H j is infinite, for at least one j ∈ {1, ...functional linear model, which corresponds to the case p = 1 in Equation (1), has been extensively ...β in the Fourier domain ... Voir le document complet
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Input-output linearization and fractional robust control of a non-linear system
... the robust control of perturbed plants using the common unity feedback ...or non-minimum-phase plants, plants with bending modes [OUS 95b], and digital control ...problems. In this paper, only the ... Voir le document complet
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Toward robust parameterized reduced-order models of non-linear structures using POD
... cost. In the present study, the efficiency of the two approaches are studied on a simple system consisting of a beam with a cubic stiffness located at the beam end (Fig ...response in a range of ... Voir le document complet
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Robust lane Extraction using Two-Dimension Declivity
... Our extraction approach has been evaluated and compared with other state- of-the-art algorithms. We selected and implemented two approaches: static thresholding and the OTSU method detailed in Sect. 2 . The ... Voir le document complet
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A NON-INTRUSIVE STRATIFIED RESAMPLER FOR REGRESSION MONTE CARLO: APPLICATION TO SOLVING NON-LINEAR EQUATIONS
... We firstly present some numerical results in dimension 1 (Tables 1 - 2 - 3 ). We have tested both the one-step (Section 3 ) and multi-step schemes (Section 4 ). The final time T is fixed to 1 and we use ... Voir le document complet
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Robust lane Extraction using Two-Dimension Declivity
... This high level of automa tion requires well-designed algorithms with high performances to deal with ail of challenging use cases usually encountered in ...and robust to achieve a dynamic ... Voir le document complet
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Bayesian Functional Linear Regression with Sparse Step Functions
... intervals in the coefficient functions from the ...being in the support of about 0.5. The results are given in Figure 8 ...higher dimension can more easily fit the ...data? In this case, ... Voir le document complet
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Linear regression-based multifidelity surrogate for disturbance amplification in multiphase explosion
... able in our problem, we imposed them using two ...30 high-fidelity data points, instead of 150 high-fidelity data points needed if symmetries are not used, reducing the simu- lation cost roughly five ... Voir le document complet
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Sequential linear regression with online standardized data
... seen in Table 3, a numerical explosion occured in most datasets using the classical methods with raw data and a variable step-size (C1 to ...noted in Table 2, these datasets had a high T 2 = T ... Voir le document complet
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Statistical Estimation in High Dimension, Sparsity and Oracle Inequalities
... M. In this chapter, we treat a new aggregation problem: D- convex aggregation which aims at constructing an aggregate which is nearly as good as the best convex combination of at most D of the M initial ... Voir le document complet
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Linear regression with stationary errors : the R package slm
... However in practice such asymptotical results are not sufficient since they do not indicate how to tune the bandwidth on a given ...of linear regression with dependent errors really more difficult ... Voir le document complet
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Non-linear regression algorithms for motor skill acquisition: a comparison
... image in the background shows the mapping from costs J k to weights P k ...visualized in the lower left corner of Figure ...then high-cost samples, and the the mean θ µ (on average) moves closer to θ ... Voir le document complet
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Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology
... the dimension of the regression variable, the activation ...used in various spectral shape analysis [4, 5] and is closely related to the modes of vibration of the ... Voir le document complet
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Minimax rate of testing in sparse linear regression
... of linear regression model under sparse ...the dimension. Non-asymptotic setting for the Gaussian mean model was analyzed by Baraud (2002) who established bounds on the minimax rate of testing ... Voir le document complet
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