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Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data

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  • Hao Zhang

Abstract

We would like to supplement this article with two more points. First, we express our views about the proposed work’s major contributions to the area of sparse functional data classification. Second, we suggest some possible future research directions and discuss ideas of generalizing the method to deal with the problem of multiclass classification for sparse functional data. Copyright Sociedad de Estadística e Investigación Operativa 2016

Suggested Citation

  • Hao Zhang, 2016. "Comments on: Probability enhanced effective dimension reduction for classifying sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 47-51, March.
    • Handle: RePEc:spr:testjl:v:25:y:2016:i:1:p:47-51
    DOI: 10.1007/s11749-015-0477-8
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    References listed on IDEAS

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    1. YichaoWu, & Liu, Yufeng, 2007. "Robust Truncated Hinge Loss Support Vector Machines," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 974-983, September.
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    3. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    4. Gareth M. James, 2002. "Generalized linear models with functional predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 411-432, August.
    5. Aurore Delaigle & Peter Hall, 2012. "Achieving near perfect classification for functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 267-286, March.
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