The problem of determining the best subset has two important aspects: the choice of a criteria defining what is meant by 'best' and a computational procedure to identify the best subset. The choice of a combination of a criteria and a computational procedure depends on the intended use of the subset model selected. Numerous criteria and computational procedures exist in literature. In this study a few of the more often used criteria and computational procedures were compared to evaluate their relative merits based on the intended use of the selected subset model. The study also incluhs an evaluation of a few methods for estimating the regression coefficients.Business, C. T. Bauer College o
Researchers with a multiple regression at hand, frequently wonder if all the independent variables a...
Originally published in 1990, the first edition of Subset Selection in Regression filled a significa...
When using multiple regression models for predictive purposes, it may be desirable to exclude some p...
This study presents comparisons of subset selection criteria used to help determine the best regre...
We address the so-called subset selection problem in multiple linear regression where the objective ...
In applied statistical studies, it is common to collect data on a large pool of candidate variables ...
This work illustrated the procedures in getting the best model using Multiple Regression. The Multip...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
The authors tries to throw a new light on some problems related to application of the regression an...
Simulation was used to evaluate the performances of several methods of variable selection in regress...
Sixteen model building and model selection procedures commonly encountered in industry, all of w...
The problem of variable selection is one of the most pervasive model selection problems in statistic...
1 page, 1 article*Selection Criteria in Multiple Regression* (Cady, Foster B.) 1 pag
International audienceIn this paper, we investigate on 39 Variable Selection procedures to give an o...
This paper introduces an alternative variable selection method for use in regression analysis that i...
Researchers with a multiple regression at hand, frequently wonder if all the independent variables a...
Originally published in 1990, the first edition of Subset Selection in Regression filled a significa...
When using multiple regression models for predictive purposes, it may be desirable to exclude some p...
This study presents comparisons of subset selection criteria used to help determine the best regre...
We address the so-called subset selection problem in multiple linear regression where the objective ...
In applied statistical studies, it is common to collect data on a large pool of candidate variables ...
This work illustrated the procedures in getting the best model using Multiple Regression. The Multip...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
The authors tries to throw a new light on some problems related to application of the regression an...
Simulation was used to evaluate the performances of several methods of variable selection in regress...
Sixteen model building and model selection procedures commonly encountered in industry, all of w...
The problem of variable selection is one of the most pervasive model selection problems in statistic...
1 page, 1 article*Selection Criteria in Multiple Regression* (Cady, Foster B.) 1 pag
International audienceIn this paper, we investigate on 39 Variable Selection procedures to give an o...
This paper introduces an alternative variable selection method for use in regression analysis that i...
Researchers with a multiple regression at hand, frequently wonder if all the independent variables a...
Originally published in 1990, the first edition of Subset Selection in Regression filled a significa...
When using multiple regression models for predictive purposes, it may be desirable to exclude some p...