LDR: A package for likelihood-based sufficient dimension reduction

R. Dennis Cook, Liliana M. Forzani, Diego R. Tomassi

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We introduce a new MATLAB software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalJournal of Statistical Software
Volume39
Issue number3
StatePublished - Mar 2011

Keywords

  • Dimension reduction
  • Inverse regression
  • Principal components

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