title: High-dimensional bolstered error estimation.
abstract: BACKGROUND
  In small-sample settings, bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap with regard to various criteria. The key issue for bolstering performance is the variance setting for the bolstering kernel. Heretofore, this variance has been determined in a non-parametric manner from the data. Although bolstering based on this variance setting works well for small feature sets, results can deteriorate for high-dimensional feature spaces.
  
  
  RESULTS
  This article computes an optimal kernel variance depending on the classification rule, sample size, model and feature space, both the original number and the number remaining after feature selection. A key point is that the optimal variance is robust relative to the model. This allows us to develop a method for selecting a suitable variance to use in real-world applications where the model is not known, but the other factors in determining the optimal kernel are known.
  
  
  BACKGROUND
  Companion website at http://compbio.tgen.org/paper_supp/high_dim_bolstering.
  
  
  BACKGROUND
  edward@mail.ece.tamu.edu.
authors.affiliation: Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA.
authors.forename: Chao
authors.initials: C
authors.lastname: Sima
authors.forename: Ulisses M
authors.initials: UM
authors.lastname: Braga-Neto
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D003627:Data Interpretation, Statistical; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D009101:Multiple Myeloma / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D018401:Sample Size