Tilted correlation screening learning in high-dimensional data analysis

Bingqing Lin, Zhen Pang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

Statistical inference can be over optimistic and even misleading based on a selected model due to the uncertainty of the model selection procedure, especially in the highdimensional data analysis. In this article, we propose a bootstrap-based tilted correlation screening learning (TCSL) algorithm to alleviate this uncertainty. The algorithm is inspired by the recently proposed variable selection method, TCS algorithm, which screens variables via tilted correlation. Our algorithm can reduce the prediction error and make the interpretation more reliable. The other gain of our algorithm is the reduced computational cost compared with the TCS algorithm when the dimension is large. Extensive simulation examples and the analysis of one real dataset are conducted to exhibit the good performance of our algorithm. Supplementary materials for this article are available online.
Original languageEnglish
Pages (from-to)478-496
Number of pages19
JournalJournal of Computational and Graphical Statistics
Volume23
Issue number2
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Bootstrap
  • Model averaging
  • TCS algorithm
  • Variable selection

ASJC Scopus subject areas

  • Discrete Mathematics and Combinatorics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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