Add to ORKGAfter a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression Methods, authors provide a different multivariate extension of the univariate PLS (1994) highlighting a different use and interpretation
Abstract: Dimension reduction is one of the major tasks for multivariate analysis, it is especially ...
<p>(A) Plot of the regression coefficients of the different regressors used in linear regression. (B...
We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives dire...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
In case one or more sets of variables are available, the use of dimensional reduction methods could ...
In case one or more sets of variables are available, the use of dimensional reduction methods could...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
A prominent difficulty facing researchers is the visualization of high dimensional data. Several dim...
In this paper, we presented a theoretical result and then discussed possible applications of our res...
Abstract: In classical multiple linear regression analysis problems will occur if the regressors are...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
Abstract — Data dimensionality refers to the number of variables that are measured on each observat...
Abstract: Dimension reduction is one of the major tasks for multivariate analysis, it is especially ...
<p>(A) Plot of the regression coefficients of the different regressors used in linear regression. (B...
We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives dire...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
After a review of the mainly dimensionality reduction methods as well as of the Shrinkage Regression...
In case one or more sets of variables are available, the use of dimensional reduction methods could ...
In case one or more sets of variables are available, the use of dimensional reduction methods could...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
A prominent difficulty facing researchers is the visualization of high dimensional data. Several dim...
In this paper, we presented a theoretical result and then discussed possible applications of our res...
Abstract: In classical multiple linear regression analysis problems will occur if the regressors are...
The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dim...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
For nearly a century, researchers have investigated and used mathematical techniques for reducing th...
Abstract — Data dimensionality refers to the number of variables that are measured on each observat...
Abstract: Dimension reduction is one of the major tasks for multivariate analysis, it is especially ...
<p>(A) Plot of the regression coefficients of the different regressors used in linear regression. (B...
We present a new methodology for sufficient dimension reduction (SDR). Our methodology derives dire...