Simulation was used to evaluate the performances of several methods of variable selection in regression modeling: stepwise regression based on partial F-tests, stepwise minimization of Mallows' C, statistic and Schwarz's Bayes Information Criterion (BIC), and regression trees constructed with two kinds of pruning. Five to 25 covariates were generated in multivariate clusters, and responses were obtained from an ordinary linear regression model involving three of the covariates; each data set had 50 observations. The regression- tree approaches were markedly inferior to the other methods in discriminating between informative and noninformative covariates, and their predictions of responses in "new" data sets were much more variable and less ...
A challenging problem in the analysis of high-dimensional data is variable selection. In this study...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
I evaluated the predictive ability of statistical models obtained by applying seven methods of varia...
Simulation was used to evaluate the performances of several methods of variable selection in regress...
In this paper, we investigate on 39 Variable Selection procedures to give an overview of the existin...
The selection of essential variables in logistic regression is vital because of its extensive use in...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
The problem of determining the best subset has two important aspects: the choice of a criteria defin...
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accurac...
Variable selection methods and model selection approaches are valuable statistical tools, which are ...
The advancement in data acquiring technology continues to see survival data sets with many covariate...
<p>The selection of suitable terms in random coefficient regression models is a challenging problem ...
This paper presents an information criteria based model selection procedure (called FIC) for choosin...
A challenging problem in the analysis of high-dimensional data is variable selection. In this study...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
I evaluated the predictive ability of statistical models obtained by applying seven methods of varia...
Simulation was used to evaluate the performances of several methods of variable selection in regress...
In this paper, we investigate on 39 Variable Selection procedures to give an overview of the existin...
The selection of essential variables in logistic regression is vital because of its extensive use in...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
In this paper, we compare the method of Gunter et al. (2011) for variable selection in treatment com...
The problem of determining the best subset has two important aspects: the choice of a criteria defin...
The selection of a descriptor, X, is crucial for improving the interpretation and prediction accurac...
Variable selection methods and model selection approaches are valuable statistical tools, which are ...
The advancement in data acquiring technology continues to see survival data sets with many covariate...
<p>The selection of suitable terms in random coefficient regression models is a challenging problem ...
This paper presents an information criteria based model selection procedure (called FIC) for choosin...
A challenging problem in the analysis of high-dimensional data is variable selection. In this study...
The goal of this paper is to compare several widely used Bayesian model selection methods in practic...
I evaluated the predictive ability of statistical models obtained by applying seven methods of varia...