Variable importance assessment in sliced inverse regression for variable selection.
Ines JlassiJérôme SaraccoPublished in: Commun. Stat. Simul. Comput. (2019)
Keyphrases
- variable selection
- linear models
- model selection
- cross validation
- proportional hazards model
- markov blanket
- input variables
- high dimensional
- stepwise regression
- dimension reduction
- regression problems
- stochastic search
- regression model
- group lasso
- high dimensional data
- number of input variables
- support vector
- neural network
- logistic regression models
- sparsity inducing
- linear regression
- ls svm
- least squares
- feature extraction
- feature selection
- machine learning
- data mining