title: Sequential selection of variables using short permutation procedures and multiple adjustments: An application to genomic data.
abstract: This work proposes a sequential methodology for selecting variables in classification problems in which the number of predictors is much larger than the sample size. The methodology includes a Monte Carlo permutation procedure that conditionally tests the null hypothesis of no association among the outcomes and the available predictors. In order to improve computing aspects, we propose a new parametric distribution, the Truncated and Zero Inflated Gumbel Distribution. The final application is to find compact classification models with improved performance for genomic data. Results using real data sets show that the proposed methodology selects compact models with optimized classification performances.
authors.affiliation: 1 Department of Industrial Engineering, Universidade Federal de Minas Gerais, Belo Horizonte,Brazil.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Azevedo Costa
authors.affiliation: 2 Computer Department, Centro Federal de Educação Tecnológica Minas Gerais, Brazil.
authors.forename: Thiago
authors.initials: T
authors.lastname: de Souza Rodrigues
authors.affiliation: 3 Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
authors.forename: André Gabriel Fc
authors.initials: AG
authors.lastname: da Costa
authors.affiliation: 4 Computer Sciences Department, University of Paris-ESIEE/Paris, Paris, France.
authors.forename: René
authors.initials: R
authors.lastname: Natowicz
authors.affiliation: 5 Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
authors.forename: Antônio
authors.initials: A
authors.lastname: Pádua Braga
mesh_terms: D000465:Algorithms; D056808:Biostatistics / Q000379:methods; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D018401:Sample Size
keywords: Classification models; extreme distributions; variable selection