波士顿国际出版社
期刊
会议
图书
作者:Feipeng Zhang , Heng Peng , Yong Zhou
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:In this paper, we study the Fine–Gray proportional subdistribution hazards model for the competing risks data under length-biased sampling. To exploit the special structure of length-biased sampling, we propose an unbiased estimating equation estimator, which can handle both...
作者:Thomas Nemmers , Anjana Narayan , Sudipto Banerjee
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:This article presents a simple and easily implementable Bayesian approach to model and quantify uncertainty in small descriptive social networks. While statistical methods for analyzing networks have seen burgeoning activity over the last decade or so, ranging from social sc...
作者:Yutao Liu , Cunjie Lin , Yong Zhou
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:A nonparametric approach is proposed to estimate the quantile residual lifetime at a given time while considering the effect of covariates. An estimating equation is constructed and a local Kaplan–Meier estimator is employed to incorporate the covariates in the equation whil...
作者:Li-Pang Chen
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:Analysis of left-truncated and right-censored (LTRC) survival data has received extensive interest. Many inference methods have been developed for the various survival models, including the Cox proportional hazards model and the transformation model. The additive hazards model is...
作者:Huijuan Ma , Jianhua Shi , Yong Zhou
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:Proportional mean residual life model is studied for analysing survival data from the case-cohort design. To simultaneously estimate the regression parameters and the baseline mean residual life function, weighted estimating equations based on an inverse selection probability are...
作者:Yaxuan Sun , Chong Wang , William Q. Meeker ...
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:Understanding the dynamics of disease spread is critical to achieving effective animal disease surveillance. A major challenge in modeling disease spread is the fact that the true disease status cannot be known with certainty due to the imperfect diagnostic sensitivity and specif...
作者:Bu Zhou , Jia Guo , Jianwei Chen ...
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:Recently, a nonparametric test for mean vectors of elliptically distributed high-dimensional data has been proposed in the literature. The asymptotic normality of the test statistic under some strong assumptions is established. In practice, however, these strong assumptions ...
作者:Xiaoyan Wang , Kuangnan Fang , Qingzhao Zhang ...
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:Linear discriminant analysis (LDA) has been extensively applied in classification. For high-dimensional data, results generated from a single dataset may be unsatisfactory because of the small sample size. Under the regression framework, integrative analysis, which pools and anal...
作者:Fengqin Tang , Yuanyuan Wang , Jinxia Su ...
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:Identifying communities is an important problem in network analysis. Various approaches have been proposed in the literature, but most of them either rely on the topological structure of the network or the node attributes, with few integrating both aspects. Here we propose a comm...
作者:Elizaveta Ivanova , Vladimir Nekrutkin
来源:[J].Statistics and Its Interface(IF 0.396), 2019, Vol.12 (1)波士顿国际出版社
摘要:The general theoretical approach to the asymptotic extraction of the signal series from the perturbed signal with the help of Singular Spectrum Analysis (briefly, SSA) was already outlined in Nekrutkin 2010, SII, v. 3, 297–319. In this paper we consider the example of such a...

我们正在为您处理中,这可能需要一些时间,请稍等。

资源合作:cnki.scholar@cnki.net, +86-10-82896619   意见反馈:scholar@cnki.net

×