Add to ORKGRT: Linear smoothers for dimension estimation Abstract: Sliced Inverse Regression (Li, 1991) is a simple nonparametric es-timation method of the structural dimension of a regression; that is, of the dimension of the linear subspace spanned by projections of the multidimen-sional regressor vector X, that contains part or all of the modelling information for the regression of a random variable Y on X. In this paper, the nonparamet-ric estimation method is extended to include the family of linear smoothers. No restrictions are placed on the distribution of the regressors except for the lin-earity condition and existence of second moments. An asymptotic chi-squared test for dimension is obtained. The theoretical results are illustrated with a s...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
Nonparametric regression is a powerful tool to estimate nonlinear relations between some predictors ...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
Sliced inverse regression and principal Hessian directions (Li, 1991, 1992) aim to reduce the dimens...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
It is well known that nonparametric regression techniques do not have good performance in high dime...
A general regression problem is one in which a response variable can be expressed as some function o...
A general regression problem is one in which a response variable can be expressed as some function o...
Sliced Inverse Regression is a method for reducing the dimensionality in multivariate non parametric...
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
This paper presents a method for testing the dimension of a general regression problem without assum...
Without parametric assumptions, high-dimensional regression analy-sis is already complex. This is ma...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
Nonparametric regression is a powerful tool to estimate nonlinear relations between some predictors ...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
Sliced inverse regression and principal Hessian directions (Li, 1991, 1992) aim to reduce the dimens...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
It is well known that nonparametric regression techniques do not have good performance in high dime...
A general regression problem is one in which a response variable can be expressed as some function o...
A general regression problem is one in which a response variable can be expressed as some function o...
Sliced Inverse Regression is a method for reducing the dimensionality in multivariate non parametric...
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
This paper presents a method for testing the dimension of a general regression problem without assum...
Without parametric assumptions, high-dimensional regression analy-sis is already complex. This is ma...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
Nonparametric regression is a powerful tool to estimate nonlinear relations between some predictors ...