Semiparametric temporal process regression of survival-out-of-hospital

Lifetime Data Analysis - The recurrent/terminal event data structure has undergone considerable methodological development in the last 10–15 years. An example of the data structure that has...     

Semiparametric regression methods for temporal processes subject to multiple sources of censoring

Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response-190 is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non-negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression parameter is estimated through a procedure that does not require estimating the baseline probability. Unlike the majority of process regression methods, the proposed methods accommodate multiple sources of censoring. In particular, we derive a computationally convenient variant of inverse probability of censoring weighting based on the additive hazards model. We show that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulations demonstrate that our estimators have good finite sample performance. We apply our method to national end-stage liver disease data. The Canadian Journal of Statistics 48: 222–237; 2020 © 2019 Statistical Society of Canada     

Estimation for Birnbaum–Saunders Distribution in Simple Step Stress–accelerated Life Test with Type-II Censoring

This paper develops the Bayesian estimation for the Birnbaum–Saunders distribution based on Type-II censoring in the simple step stress–accelerated life test with power law accelerated form. Maximum likelihood estimates are obtained and Gibbs sampling procedure is used to get the Bayesian estimates for shape parameter of Birnbaum–Saunders distribution and parameters of power law–accelerated model. Asymptotic normality method and Markov Chain Monte Carlo method are employed to construct the corresponding confidence interval and highest posterior density interval at different confidence level, respectively. At last, the results are compared by using Monte Carlo simulations, and a numerical example is analyzed for illustration.     

历史分期命名标准刍议

大半个世纪以来，中国历史分期聚讼未决，原因之一是“封建”等核心术语概念有待厘清。本文建议，在给历史段落命名时，应“制名以指实”、“循旧以造新”、“中外义通约”、“形与义切合”。秦以下两千年命名“封建社会”，名实错位、形义脱节，亦与外来义相左，而称之“宗法地主专制社会”较为名实相符。     

泛化“封建”观有悖马克思的封建论

冯天瑜认为,将周秦之际至清中叶的两千多年中国社会称之“封建社会”,曾被视为“马克思主义史学”结论,并长期得以沿用;然而,认真研读马、恩论著,尤其是马克思晚年的民族学笔记,便会发现,这种看法其实是一种误解。将秦汉至明清称“封建社会”的泛化封建观,是与马克思、恩格斯的封建社会原论相悖的。陈启云认为,传统中国究竟是属于“封建型”抑或是“大一统”、“专制”、“独裁”型,如果从“范畴理论”而言,这两者是绝不相容的;但从“历史事实”而言,这两者却常常是并存的——有时是先后轮转(如春秋—战国—秦汉—魏晋),多数情况下是同时并存(如西周、战国、秦汉、隋唐、辽金元)。张国刚认为,我们在看到历史的差异性和各自特色的同时,也要认识到人类历史发展中的同一性,所以,关于历史阶段划分,既可以用唯物史观的标准,把中国的中世纪叫做封建社会;也可以按照生产力发展水平,用上古、中古、近代来划分;还可以用时序如史前、族邦、皇权、共和等概念来划分历史阶段,使国内外学者能够在学术上做到沟通和理解。许苏民认为,冯天瑜提出的关于自秦迄清中国社会的性质是“宗法地主专制社会”或“地主社会”的观点是值得商榷的,根据马克思主义的政治与经济之统一的标准,自秦迄清中国社会性质实际上是皇权官僚专制社会。     

五四时期陈独秀"反封建"命题评析

以"反封建"作为中国近代民主运动的中心题旨,是陈独秀从日本"大正德谟克拉西"借来的一种法兰西式的激进主义表述.陈氏的"反封建"命题忽视了中、欧、日历史的巨大的差异,开泛化"封建"之先河.因此,有必要重新反思五四"反封建"的口号.     

依托优势坚持特色错位竞争——以北京化工大学物流管理新专业建设为例

物流管理专业是我国高等教育中的新兴专业,目前很多高校都在进行物流管理专业建设方面的探索研究。本文以北京化工大学的物流管理专业建设为背景,创新培养理念,提出物流管理专业应以“依托优势、坚持特色、错位竞争”为指导原则,并对培养目标、专业方向和课程体系设置等方面进行了深入的探讨。希冀对物流管理专业的建设提供一定的借鉴。     

辽代锦州临海军节度使出使外交考

辽太祖时期设立锦州临海军节度使,现可考历任锦州临海军节度使（节度副使）有赵思温等22位官员。临海军节度使除了负责地方军政事务以外,大部分还出任了出使外交的使臣,主要任务有“贺生辰”、“贺正旦”、“告哀”、“祭奠”等,这些出使外交活动对于辽朝与中原地区的经济文化交流乃至政治社会的稳定意义深远。     

Residual life estimation under time-varying conditions based on a Wiener process

Residual life (RL) estimation plays an important role in prognostics and health management. In operating conditions, components usually experience stresses continuously varying over time, which have an impact on the degradation processes. This paper investigates a Wiener process model to track and predict the RL under time-varying conditions. The item-to-item variation is captured by the drift parameter and the degradation characteristic of the whole population is described by the diffusion parameter. The bootstrap method and Bayesian theorem are employed to estimate and update the distribution parameters of ‘a’ and ‘b’, which are the coefficients of the linear drifting process in the degradation model. Once new degradation information becomes available, the RL distributions considering the future operating condition are derived. The proposed method is tested on Lithium-ion battery devices under three levels of charging/discharging rates. The results are further validated by a simulation method.