Examples of using Variable selection in English and their translations into Portuguese
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Introduction to variable selection technique.
The backward method Wald was used for variable selection.
A stepwise variable selection process was employed.
A forward likelihood ratio approach was applied for variable selection.
For variable selection was used the stepwise backward method.
In this thesis we study several aspects of the variable selection problem.
For the purpose of variable selection, a 5% significance level was used.
After univariate analysis,there was a multivariate stepwise criterion variable selection.
The backward method for variable selection was used as no explanatory model was available.
A control element on the sensor allows for intuitive setting of the zero point and variable selection of the distance measurement range.
The criterion for model variable selection was a p value< 0.200 in the univariate analysis.
For the adjusted analysis, we used the method of stepwise variable selection with backward elimination.
The stepwise variable selection method with backward elimination was used in the adjusted analysis.
In this context, this work proposes a gpu-based af(af-rlm) for variable selection using multiple linear regression models rlm.
After variable selection for this study, the percentage of unknown data on age and place of residence was evaluated.
This thesis proposes methods for variable selection aimed at classifying production batches.
Variable selection in statistical models is an important problem, for which many different solutions have been proposed.
This thesis test an approach to variable selection aimed at clustering and classifying drug samples.
Variable selection was performed by the stepwise method by elimination, and the significance level was set at 10.
This thesis proposes new approaches for variable selection aimed at forming representative groups of observations.
The variable selection process used in the regression analysis was the stepwise, in which, at each step, all combinations are tested.
Different strategies of forecast combinations and variable selection procedures for multivariate methods were contemplated.
As the variable selection criterion, we used the entropy index calculation to measure the heterogeneity and the information gain.
Traditional algorithms as successive projections algorithm(aps)have been quite used for variable selection in multivariate calibration problems.
The combination of variable selection with a non-linear method(ga-svm-c) provided the best classification result f1 1.00.
This work proposes the use of multi-objective evolutionary algorithm on tables(aemt) for variable selection in classification problems, using linear discriminant analysis.
For the purpose of variable selection, two techniques were utilized, namely the successive projections algorithm(spa) and stepwise(sw) formulation.
Our program is spread across 30 credits and contains projects involving big datasets,classification methods, variable selection, and deep learning to name a few.
The final model was built by forward stepwise variable selection with entry and exit criteria at the p 0.05 and p 0.1 levels, respectively.
The evaluation of the main factors related to survival rate used the Cox regression analysis; univariate andmultiple models with the stepwise criterion for variable selection.