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of 106
pro vyhledávání: '"Jiang, Ci"'
Hierarchical multi-label classification (HMC) has drawn increasing attention in the past few decades. It is applicable when hierarchical relationships among classes are available and need to be incorporated along with the multi-label classification w
Externí odkaz:
http://arxiv.org/abs/2205.07833
Functional Principal Component Analysis (FPCA) has become a widely-used dimension reduction tool for functional data analysis. When additional covariates are available, existing FPCA models integrate them either in the mean function or in both the me
Externí odkaz:
http://arxiv.org/abs/2204.05622
Akademický článek
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In this article we propose a novel ranking algorithm, referred to as HierLPR, for the multi-label classification problem when the candidate labels follow a known hierarchical structure. HierLPR is motivated by a new metric called eAUC that we design
Externí odkaz:
http://arxiv.org/abs/1810.07954
Autor:
Chen, Lu-Hung, Jiang, Ci-Ren
The focus of this paper is to extend Fisher's linear discriminant analysis (LDA) to both densely re-corded functional data and sparsely observed longitudinal data for general $c$-category classification problems. We propose an efficient approach to i
Externí odkaz:
http://arxiv.org/abs/1606.03844
Autor:
Chen, Lu-Hung, Jiang, Ci-Ren
Functional principal component analysis is one of the most commonly employed approaches in functional and longitudinal data analysis and we extend it to analyze functional/longitudinal data observed on a general $d$-dimensional domain. The computatio
Externí odkaz:
http://arxiv.org/abs/1510.04439
Positron Emission Tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on t
Externí odkaz:
http://arxiv.org/abs/1411.2051
Publikováno v:
Annals of Statistics 2014, Vol. 42, No. 2, 563-591
Sliced inverse regression (Duan and Li [Ann. Statist. 19 (1991) 505-530], Li [J. Amer. Statist. Assoc. 86 (1991) 316-342]) is an appealing dimension reduction method for regression models with multivariate covariates. It has been extended by Ferr\'{e
Externí odkaz:
http://arxiv.org/abs/1405.6017
Autor:
Jiang, Ci-Ren, Wang, Jane-Ling
Publikováno v:
Annals of Statistics 2011, Vol. 39, No. 1, 362-388
A new single-index model that reflects the time-dynamic effects of the single index is proposed for longitudinal and functional response data, possibly measured with errors, for both longitudinal and time-invariant covariates. With appropriate initia
Externí odkaz:
http://arxiv.org/abs/1103.1726
Autor:
Jiang, Ci-Ren, Wang, Jane-Ling
Publikováno v:
Annals of Statistics 2010, Vol. 38, No. 2, 1194-1226
Classical multivariate principal component analysis has been extended to functional data and termed functional principal component analysis (FPCA). Most existing FPCA approaches do not accommodate covariate information, and it is the goal of this pap
Externí odkaz:
http://arxiv.org/abs/1003.0261