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Multiclass analysis and prediction with network structured covariates

Author

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  • Li-Pang Chen

    (Department of Statistics and Actuarial Science, University of Waterloo)

  • Grace Y. Yi

    (Department of Statistics and Actuarial Science, University of Waterloo)

  • Qihuang Zhang

    (Department of Statistics and Actuarial Science, University of Waterloo)

  • Wenqing He

    (Department of Statistical and Actuarial Sciences, University of Western Ontario)

Abstract

Technological advances associated with data acquisition are leading to the production of complex structured data sets. The recent development on classification with multiclass responses makes it possible to incorporate the dependence structure of predictors. The available methods, however, are hindered by the restrictive requirements. Those methods basically assume a common network structure for predictors of all subjects without taking into account the heterogeneity existing in different classes. Furthermore, those methods mainly focus on the case where the distribution of predictors is normal. In this paper, we propose classification methods which address these limitations. Our methods are flexible in handling possibly class-dependent network structures of variables and allow the predictors to follow a distribution in the exponential family which includes normal distributions as a special case. Our methods are computationally easy to implement. Numerical studies are conducted to demonstrate the satisfactory performance of the proposed methods.

Suggested Citation

  • Li-Pang Chen & Grace Y. Yi & Qihuang Zhang & Wenqing He, 2019. "Multiclass analysis and prediction with network structured covariates," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-25, December.
    • Handle: RePEc:spr:jstada:v:6:y:2019:i:1:d:10.1186_s40488-019-0094-2
    DOI: 10.1186/s40488-019-0094-2
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Cai, Wei & Guan, Guoyu & Pan, Rui & Zhu, Xuening & Wang, Hansheng, 2018. "Network linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 32-44.
    3. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    4. Safo, Sandra E. & Ahn, Jeongyoun, 2016. "General sparse multi-class linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 81-90.
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    Cited by:

    1. Li-Pang Chen, 2022. "Network-Based Discriminant Analysis for Multiclassification," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 410-431, November.

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