Frailty modelling approaches for semi-competing risks data

Lifetime Data Analysis - In the semi-competing risks situation where only a terminal event censors a non-terminal event, observed event times can be correlated. Recently, frailty models with an...     

Bayesian inference for zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables

Abstract

In the fields of internet financial transactions and reliability engineering, there could be more zero and one observations simultaneously. In this paper, considering that it is beyond the range where the conventional model can fit, zero-and-one-inflated geometric distribution regression model is proposed. Ingeniously introducing Pólya-Gamma latent variables in the Bayesian inference, posterior sampling with high-dimensional parameters is converted to latent variables sampling and posterior sampling with lower-dimensional parameters, respectively. Circumventing the need for Metropolis-Hastings sampling, the sample with higher sampling efficiency is obtained. A simulation study is conducted to assess the performance of the proposed estimation for various sample sizes. Finally, a doctoral dissertation data set is analyzed to illustrate the practicability of the proposed method, research shows that zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables can achieve better fitting results.     

Functional Partial Linear Single‐index Model

This paper deals with the problem of predicting the real‐valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single‐index model treats the multivariate random variable as linear part and the random curve as functional single‐index part, respectively. To estimate the non‐parametric link function, the functional single‐index and the parameters in the linear part, a two‐stage estimation procedure is proposed. Compared with existing semi‐parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single‐index. The convergence rate for the non‐parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real‐data analysis are conducted to evaluate the empirical performance of the proposed method.     

率性火辣美:船山美学的湖湘文化基因

王船山美学中有独具火辣湖湘文化特色的“率性火辣美”现象,表现为审美标准上的强悍火辣美,审美情趣上的率性爽辣美,审美批评上的倔拗冲辣美。     

修复式司法在校园霸凌中的适用

我国传统的报应式刑事司法模式已经难以应对日趋严峻的校园霸凌以及由此引发的未成年人犯罪案件,有必要引入修复式司法以弥补报应式司法的不足。如果只是为了实现个案正义,那么,再怎么降低刑事责任年龄都不啻为削足适履,而且极有可能使得被贴上"犯罪标签"的未成年人产生恶性循环,诱发更严重的犯罪行为。修复式司法把被害人疗愈和加害人自愿承担责任作为目标,注重双方以平等的方式沟通交流,使双方有机会了解事情的真相,最大程度地满足实质需求,最终修复被犯罪所破坏的关系。     

中国新诗发生与现代媒体

现代性存在于历史的时空里并且作为一个历史的过程演进着。文学的现代性研究差不多已成为时髦而老调的话题,其缺失之一在于研究的空泛无着。中国文学由古典型态转化为现代型态,或者说中国文学现代文体的发生,原本是多种合力共同作用的结果。文体的现代性是文学现代性重要的可感存在。媒体,作为一个较为具体的视窗,由此可观览中国现代文体发生的图景。中国现代文体的发生不仅提供了物化的条件,而且由于媒体生态关联性的影响,使得基于媒体的文体在形态与精神等方面,生成了异于过往的诸多质性。媒体对于中国现代文体发生的意义颇多。通过媒体这一视角,解析诗歌、散文、小说、戏剧四种文体,无疑是研究中国文学现代转型的具体路径和方法。     

降低刑事责任年龄否定论

降低刑事责任年龄的提议主要立足于三个命题:“犯罪低龄化”现象严重,降低刑事责任年龄是解决“低龄犯罪人”问题的应然手段,低龄未成年人已具备相当的辨认、控制能力。通过对实证数据与相关论据的收集分析,前两个命题得到证伪,第三个命题无法证明,由此可以否定降低刑事责任年龄观点的合理性依据。从我国历史与近代刑事责任年龄设定的发展历程来看,调整年龄节点的主张从未停止,援用的理由相差无几,经历过多次变动与论证后延续至今,具备符合我国社会发展实践的合理性与稳定性。从我国的义务教育设置与年龄之间的对应关系出发,刑事责任年龄确定的基准点应以义务教育的结束为合理节点,采取“从高到低”的刑事责任年龄设定逻辑。综合上述理由,兼顾少年司法“教育为主、惩罚为辅”刑事政策导向,最终得出当前的刑事责任年龄设置合理,不需降低的结论。     

江西推进科技金融发展的问题及对策建议

推进科技金融发展对于江西加快建设创新型省份、助推金融业高质量发展及支持民营经济发展等具有重要意义。近年来,江西在完善科技金融政策体系、产品投融资体系和服务体系上取得了明显成效,也存在职能部门统筹联动待加强、科技金融有效供给不充分、化解信息不对称较困难和科技金融统计体系待构建等问题。笔者建议,江西应完善系统化顶层设计,推动产品创新和服务完善,大力吸引金融机构和人才,完善科技金融领域基础设施建设,推进科技金融健康高效发展。     

An elastic-net penalized expectile regression with applications

To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.