Add to ORKGAnalysis of data sets that involve large numbers of variables usually entails some type of model fitting and data reduction. In regression problems, a fitted model that is obtained by a selection process can be difficult to evaluate because of optimism induced by the choice mechanism. Problems in areas such as discriminant analysis, calibration, and the like often lead to similar difficulties. The preceeding sections reviewed some of the general ideas behind assessment of regression-type predictors and illustrated how they can be easily incorporated into a standard data analysis
In predictive modeling and data mining one is often confronted with a large number of inputs (explan...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
• Towards general principles for model selection. • ‘Give up your small ambitions’ • Check your assu...
model selection, model evaluation, Akaike's information criterion, AIC, Schwarz's, criterion, cluste...
The problem of determining the best subset has two important aspects: the choice of a criteria defin...
Simulation was used to evaluate the performances of several methods of variable selection in regress...
The problem of statistical model selection in econometrics and statistics is reviewed. Model selecti...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...
Model selection methods provide a way to select one model among a set of models in a statistically v...
In applied statistical studies, it is common to collect data on a large pool of candidate variables ...
Specifying a regression model requires deciding on which explanatoryvariables it should contain as w...
In this paper, we investigate on 39 Variable Selection procedures to give an overview of the existin...
Sixteen model building and model selection procedures commonly encountered in industry, all of w...
This talk begins with a contrast of exploratory data analysis (a la Tukey) and formal analysis. Cha...
The problem of variable selection is one of the most pervasive model selection problems in statistic...
In predictive modeling and data mining one is often confronted with a large number of inputs (explan...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
• Towards general principles for model selection. • ‘Give up your small ambitions’ • Check your assu...
model selection, model evaluation, Akaike's information criterion, AIC, Schwarz's, criterion, cluste...
The problem of determining the best subset has two important aspects: the choice of a criteria defin...
Simulation was used to evaluate the performances of several methods of variable selection in regress...
The problem of statistical model selection in econometrics and statistics is reviewed. Model selecti...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...
Model selection methods provide a way to select one model among a set of models in a statistically v...
In applied statistical studies, it is common to collect data on a large pool of candidate variables ...
Specifying a regression model requires deciding on which explanatoryvariables it should contain as w...
In this paper, we investigate on 39 Variable Selection procedures to give an overview of the existin...
Sixteen model building and model selection procedures commonly encountered in industry, all of w...
This talk begins with a contrast of exploratory data analysis (a la Tukey) and formal analysis. Cha...
The problem of variable selection is one of the most pervasive model selection problems in statistic...
In predictive modeling and data mining one is often confronted with a large number of inputs (explan...
When performing a regression or classification analysis, one needs to specify a statistical model. T...
• Towards general principles for model selection. • ‘Give up your small ambitions’ • Check your assu...