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ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction

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  • Adragni, Kofi Placid
  • Raim, Andrew M.

Abstract

In regression settings, a sufficient dimension reduction (SDR) method seeks the core information in a p-vector predictor that completely captures its relationship with a response. The reduced predictor may reside in a lower dimension d

Suggested Citation

  • Adragni, Kofi Placid & Raim, Andrew M., 2014. "ldr: An R Software Package for Likelihood-Based Sufficient Dimension Reduction," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i03).
    • Handle: RePEc:jss:jstsof:v:061:i03
    DOI: http://hdl.handle.net/10.18637/jss.v061.i03
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    References listed on IDEAS

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    1. Weisberg, Sanford, 2002. "Dimension Reduction Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i01).
    2. Cook, R. Dennis & Ni, Liqiang, 2005. "Sufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 410-428, June.
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    Cited by:

    1. Baek, Seungchul & Hoyoung, Park & Park, Junyong, 2024. "Variable selection using data splitting and projection for principal fitted component models in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).

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