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Ye Peng1; Sun Liuquan1; Zhao Xingqiu2; Xu Wei3
Source Publicationsciencechinamathematics
AbstractMultivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.
Funding Project[National Natural Science Foundation of China] ; [Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences] ; [Beijing Center for Mathematics and Information Interdisciplinary Sciences] ; [Research Grant Council of Hong Kong] ; [Hong Kong Polytechnic University]
Document Type期刊论文
Recommended Citation
GB/T 7714
Ye Peng,Sun Liuquan,Zhao Xingqiu,et al. anadditivemultiplicativeratesmodelformultivariaterecurrenteventswitheventcategoriesmissingatrandom[J]. sciencechinamathematics,2015,58(6):1163.
APA Ye Peng,Sun Liuquan,Zhao Xingqiu,&Xu Wei.(2015).anadditivemultiplicativeratesmodelformultivariaterecurrenteventswitheventcategoriesmissingatrandom.sciencechinamathematics,58(6),1163.
MLA Ye Peng,et al."anadditivemultiplicativeratesmodelformultivariaterecurrenteventswitheventcategoriesmissingatrandom".sciencechinamathematics 58.6(2015):1163.
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