LDR: a package for likelihood-based sufficient dimension reduction.We introduce a software package running under Matlab 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 operations, a graphical user interface is also provided for prototype computations.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Prendergast, Luke A.; Healey, Alan F.: Improving estimated sufficient summary plots in dimension reduction using minimization criteria based on initial estimates (2016)
- Lindsey, Charles D.; Sheather, Simon J.; Mckean, Joseph W.: Using sliced mean variance-covariance inverse regression for classification and dimension reduction (2014)
- Schott, James R.: A note on maximum likelihood estimation for covariance reducing models (2012)
- Velilla, Santiago: On the structure of the quadratic subspace in discriminant analysis (2010)
- Cook, Dennis; Forzani, Liliana; Tomassi, Diego: LDR a package for likelihood-based sufficient dimension reduction (2009)