International audienceIn "Li, L. and Yin, X. (2007). Sliced Inverse Regression with Regularizations., Biometrics, 64(1), 124-131" a ridge SIR estimator is introduced as the solution of a minimization problem and computed thanks to an alternating least-squares algorithm. This methodology reveals good performance in practice. In this note, we focus on the theoretical properties of the estimator. Is it shown that the minimization problem is degenerated in the sense that only two situations can occur: Either the ridge SIR estimator does not exist or it is zero
International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimens...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
The focus is on the functional regression model in which a real random variable has to be predicted ...
International audienceSliced Inverse Regression (SIR) is an effective method for dimension reduction...
International audienceSliced Inverse Regression (SIR) is an effective method for dimension reduction...
Since its introduction in the early 90's, the Sliced Inverse Regression (SIR) methodology has evolve...
Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Sev...
In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The man...
We develop an extension of sliced inverse regression (SIR) that we call localized sliced inverse reg...
Among methods to analyze high-dimensional data, the sliced inverse regression (SIR) is of particular...
We consider a semiparametric single index regression model involving a p-dimensional quantitative co...
Sliced inverse regression (SIR) is a dimension reduction technique that is both efficient and simple...
Why is ridge regression (RR) often a useful method even in cases where multiple linear regression (M...
This thesis proposes three extensions of Sliced Inverse Regression namely: Collaborative SIR, Stude...
In this article, we focus on data arriving sequentially by blocks in a stream. A semiparametric regr...
International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimens...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
The focus is on the functional regression model in which a real random variable has to be predicted ...
International audienceSliced Inverse Regression (SIR) is an effective method for dimension reduction...
International audienceSliced Inverse Regression (SIR) is an effective method for dimension reduction...
Since its introduction in the early 90's, the Sliced Inverse Regression (SIR) methodology has evolve...
Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Sev...
In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The man...
We develop an extension of sliced inverse regression (SIR) that we call localized sliced inverse reg...
Among methods to analyze high-dimensional data, the sliced inverse regression (SIR) is of particular...
We consider a semiparametric single index regression model involving a p-dimensional quantitative co...
Sliced inverse regression (SIR) is a dimension reduction technique that is both efficient and simple...
Why is ridge regression (RR) often a useful method even in cases where multiple linear regression (M...
This thesis proposes three extensions of Sliced Inverse Regression namely: Collaborative SIR, Stude...
In this article, we focus on data arriving sequentially by blocks in a stream. A semiparametric regr...
International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimens...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
The focus is on the functional regression model in which a real random variable has to be predicted ...