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LDR: A Package for Likelihood-Based Sufficient Dimension Reduction

Author

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

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.

Suggested Citation

  • Cook, R. Dennis & Forzani, Liliana M. & Tomassi, Diego R., 2011. "LDR: A Package for Likelihood-Based Sufficient Dimension Reduction," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i03).
    • Handle: RePEc:jss:jstsof:v:039:i03
    DOI: http://hdl.handle.net/10.18637/jss.v039.i03
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    Cited by:

    1. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    2. Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.
    3. Schott, James R., 2012. "A note on maximum likelihood estimation for covariance reducing models," Statistics & Probability Letters, Elsevier, vol. 82(9), pages 1629-1631.
    4. repec:jss:jstsof:39:i03 is not listed on IDEAS
    5. Charles Lindsey & Simon Sheather & Joseph McKean, 2014. "Using sliced mean variance–covariance inverse regression for classification and dimension reduction," Computational Statistics, Springer, vol. 29(3), pages 769-798, June.
    6. Chiancone, Alessandro & Forbes, Florence & Girard, Stéphane, 2017. "Student Sliced Inverse Regression," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 441-456.

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