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Exploiting predictor domain information in sufficient dimension reduction

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  • Li, Lexin

Abstract

Analysis of high-dimensional data is becoming the norm in a variety of scientific studies and dimension reduction methods are widely employed. As the predictor domain knowledge is often available, it is useful to incorporate such domain information into dimension reduction and subsequent model formulation. Existing solutions such as simple average, principal components analysis and partial least squares cannot assure preservation of full regression information when reducing the dimension. In this article we investigate sufficient dimension reduction strategies that can retain full regression information meanwhile utilizing prior domain knowledge. Both simulations and a real data analysis demonstrate that the new methods are effective and often superior than the existing solutions.

Suggested Citation

  • Li, Lexin, 2009. "Exploiting predictor domain information in sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2665-2672, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2665-2672
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    References listed on IDEAS

    as
    1. Lexin Li & R. Dennis Cook & Chih-Ling Tsai, 2007. "Partial inverse regression," Biometrika, Biometrika Trust, vol. 94(3), pages 615-625.
    2. Li, Bing & Wang, Shaoli, 2007. "On Directional Regression for Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 997-1008, September.
    3. Xiangrong Yin & R. Dennis Cook, 2005. "Direction estimation in single-index regressions," Biometrika, Biometrika Trust, vol. 92(2), pages 371-384, June.
    4. 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. Zifang Guo & Lexin Li & Wenbin Lu & Bing Li, 2015. "Groupwise Dimension Reduction via Envelope Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1515-1527, December.
    2. 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).
    3. Xinyi Xu & Jingxiao Zhang, 2020. "Groupwise sufficient dimension reduction via conditional distance clustering," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(2), pages 217-242, February.
    4. Peter Radchenko & Xinghao Qiao & Gareth M. James, 2015. "Index Models for Sparsely Sampled Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 824-836, June.
    5. Junmin Liu & Deli Zhu & Luoyao Yu & Xuehu Zhu, 2023. "Specification testing of partially linear single-index models: a groupwise dimension reduction-based adaptive-to-model approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 232-262, March.
    6. Yang Liu & Francesca Chiaromonte & Bing Li, 2017. "Structured Ordinary Least Squares: A Sufficient Dimension Reduction approach for regressions with partitioned predictors and heterogeneous units," Biometrics, The International Biometric Society, vol. 73(2), pages 529-539, June.
    7. Xuehu Zhu & Jun Lu & Jun Zhang & Lixing Zhu, 2021. "Testing for conditional independence: A groupwise dimension reduction‐based adaptive‐to‐model approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 549-576, June.

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