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 Garthwaite's approach (1994) highlighting a different use and interpretation
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data s...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
Dimension reduction in a regression analysis of response y given a p-dimensional vector of predictor...
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...
Lo studio delle relazioni tra più variabili esplicative e più dipendenti spesso richiede l’utilizzo ...
Dimensional Analysis (DA) is a mathematical method that manipulates the data to be analyzed in a hom...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
In this paper, we presented a theoretical result and then discussed possible applications of our res...
<p>(A) Plot of the regression coefficients of the different regressors used in linear regression. (B...
A prominent difficulty facing researchers is the visualization of high dimensional data. Several dim...
Abstract: In classical multiple linear regression analysis problems will occur if the regressors are...
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data s...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
Dimension reduction in a regression analysis of response y given a p-dimensional vector of predictor...
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...
Lo studio delle relazioni tra più variabili esplicative e più dipendenti spesso richiede l’utilizzo ...
Dimensional Analysis (DA) is a mathematical method that manipulates the data to be analyzed in a hom...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
In this paper, we presented a theoretical result and then discussed possible applications of our res...
<p>(A) Plot of the regression coefficients of the different regressors used in linear regression. (B...
A prominent difficulty facing researchers is the visualization of high dimensional data. Several dim...
Abstract: In classical multiple linear regression analysis problems will occur if the regressors are...
Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data s...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
Dimension reduction in a regression analysis of response y given a p-dimensional vector of predictor...