Multiclass analysis and prediction with network structured covariates

Li-Pang Chen (), Grace Y. Yi (), Qihuang Zhang () and Wenqing He ()
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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

Journal of Statistical Distributions and Applications, 2019, vol. 6, issue 1, 1-25

Abstract: 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.

Keywords: F-score; Logistic regression model; Multiclassification; Network structure (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1186/s40488-019-0094-2

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