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[PDF] Top 20 Variable selection and estimation in multivariate functional linear regression via the Lasso

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Variable selection and estimation in multivariate functional linear regression via the Lasso

Variable selection and estimation in multivariate functional linear regression via the Lasso

... knowledge, the model has been first mentioned in the work of Cardot et ...under the name of multiple functional linear ...algorithm and applied to the ozone ... Voir le document complet

21

VARIABLE SELECTION AND ESTIMATION IN MULTIVARIATE FUNCTIONAL LINEAR REGRESSION VIA THE LASSO

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 ... Voir le document complet

36

Contributions to variable selection, clustering and statistical estimation inhigh dimension

Contributions to variable selection, clustering and statistical estimation inhigh dimension

... from the optimal rate ⇤ exp under exponential tails, in particular, from the rate under the sub-Gaussian ...results in the literature on sparse regression ...Gautier ... Voir le document complet

245

Bayesian Functional Linear Regression with Sparse Step Functions

Bayesian Functional Linear Regression with Sparse Step Functions

... Conclusion In this paper, we have provided a full Bayesian methodology to analyse linear models with time-dependent functional ...covariates. The main purpose of our study was to es- timate ... Voir le document complet

26

Slope heuristics for variable selection and clustering via Gaussian mixtures

Slope heuristics for variable selection and clustering via Gaussian mixtures

... more and more concerned with large datasets where ob- servations are described by many ...Nevertheless, the useful information for clustering can be contained into a variable subset and some ... Voir le document complet

36

Sparse Oracle Inequalities for Variable Selection via Regularized Quantization

Sparse Oracle Inequalities for Variable Selection via Regularized Quantization

... weighted Lasso type procedure adapted to k-means, as suggested in [ 24 ...passing the weights proposed in [ 24 ] as well as those proposed in [ 28 ] in a Generalized ... Voir le document complet

11

Estimation of the noise covariance operator in functional linear regression with functional outputs

Estimation of the noise covariance operator in functional linear regression with functional outputs

... random variable, independent of X. The functional variables X, Y and ε are random functions taking values on the interval I = [0, 1] of ...b] in what follows. For the sake ... Voir le document complet

15

Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics

Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics

... (Hendry and Krolzig, 1999, and Krolzig and Hendry, 2001), and more recently in Autometrics (Doornik, 2009), which will examined in this ...techniques and their ...during ... Voir le document complet

28

Model selection via the lasso in conditional logistic regression

Model selection via the lasso in conditional logistic regression

... so-called lasso. This penalty, which shrinks coefficients to improve the accuracy of prediction, is particularly adapted when the number of covariates is large (with respect to the number of ... Voir le document complet

2

Histogram selection in non gaussian regression

Histogram selection in non gaussian regression

... with the problem of choosing an histogram estimator of a regression function s mapping X into ...adopt the non asymptotic approach of model selection via penalization devel- opped by ... Voir le document complet

20

Ant Colony based model selection for functional-input Gaussian process regression

Ant Colony based model selection for functional-input Gaussian process regression

... represent the ones going back from the food source to the ...System In ACO algorithms, a colony of artificial ants evaluate solutions to the optimization problem at ...hand. The ... Voir le document complet

18

On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection

On combining wavelets expansion and sparse linear models for Regression on metabolomic data and biomarker selection

... Perpignan Via Domitia, IUT, Dpt STID, F-66860 Perpignan - France 3 Advanced Technologies Application Center, CENATAV, Havana - Cuba 4 INRA, Unit´e M´et@risk, AgroParisTech, 16 rue Claude Bernard, F-75005 Paris - ... Voir le document complet

46

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso

Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso

... when the SNR is high (left), all estimators (except ` 2,1 -MLE) reach the (0, 1) ...that the estimated support is exactly the true one. However, when the SNR decreases (middle), ... Voir le document complet

32

Biosignals for driver's stress level assessment : functional variable selection and fractal characterization

Biosignals for driver's stress level assessment : functional variable selection and fractal characterization

... Over the past decades, several studies have confirmed the intrinsic fractal properties in a variety of physiological signals and such characterization was shown to be in close link with ... Voir le document complet

141

Application of linear functional observers for the thermal estimation in power modules

Application of linear functional observers for the thermal estimation in power modules

... show the feasibility of our approach to set fast ...modules. and the proposed method for design minimal order observers may be easily applied on these ...models. The only difference lies ... Voir le document complet

14

Kernel Selection in Nonparametric Regression

Kernel Selection in Nonparametric Regression

... proposed in order to select the band- width of Parzen-Rosenblatt’s estimator (` = 1 and K defined by ...introduced in [10], which reaches the adequate bias-variance compromise, but is ... Voir le document complet

24

Template estimation for samples of curves and functional calibration estimation via the method of maximum entropy on the mean

Template estimation for samples of curves and functional calibration estimation via the method of maximum entropy on the mean

... genes in a given ...obtained and then one of the most popular applications is to compare gene expression levels on different conditions, which leads to millions of measures of gene expression levels ... Voir le document complet

136

Estimation bayésienne du lasso adaptatif pour l'issue

Estimation bayésienne du lasso adaptatif pour l'issue

... du lasso adaptatif pour l'issue a été développée pour l'identication de facteurs confondants pour l'estimation non biaisée de l'eet d'une exposition binaire sur une ... Voir le document complet

53

Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

Simultaneous estimation of the mean and the variance in heteroscedastic Gaussian regression

... The first one, called the bias term, represents the capacity of S m × Σ m to approximate the true value of (s, ...σ). The second, called the variance term, is proportional to ... Voir le document complet

29

Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression

Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression

... compare the true number of visitors during the test phase with the predicted values of the four ...(especially in the afternoon) the closest one to the truth. ... Voir le document complet

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