Add to ORKGIn this paper, we presented a theoretical result and then discussed possible applications of our result to SDR problems. In addition to providing insights into existing SDR methods when Y is univariate; our theorem also applies to multivariate responses, especially when the response takes the form of (Y,W), where Y is a continuous variable and W is categorical.Sufficient dimension reduction Slicing Multivariate dimension reduction Censoring regression
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
Sufficient dimension reduction (SDR) is a class of supervised dimension reduction techniques which g...
In this paper, we presented a theoretical result and then discussed possible applications of our res...
We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives dire...
Because of the advances of modern technology, the size of the collected data nowadays is larger and ...
Without parametric assumptions, high-dimensional regression analy-sis is already complex. This is ma...
Sufficient dimension reduction methodologies in regression have been developed in the past decade, f...
In case one or more sets of variables are available, the use of dimensional reduction methods could...
A regression model where the response as well as the explaining variables are time series is conside...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
In case one or more sets of variables are available, the use of dimensional reduction methods could ...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
Sufficient dimension reduction (SDR) methods target finding lower-dimensional representations of a m...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
Sufficient dimension reduction (SDR) is a class of supervised dimension reduction techniques which g...
In this paper, we presented a theoretical result and then discussed possible applications of our res...
We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives dire...
Because of the advances of modern technology, the size of the collected data nowadays is larger and ...
Without parametric assumptions, high-dimensional regression analy-sis is already complex. This is ma...
Sufficient dimension reduction methodologies in regression have been developed in the past decade, f...
In case one or more sets of variables are available, the use of dimensional reduction methods could...
A regression model where the response as well as the explaining variables are time series is conside...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
In case one or more sets of variables are available, the use of dimensional reduction methods could ...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
Sufficient dimension reduction (SDR) methods target finding lower-dimensional representations of a m...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
Sufficient dimension reduction (SDR) is a class of supervised dimension reduction techniques which g...