A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems

The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards model and the accelerated failure time (AFT) model. Rank-based estimation in the AFT model is computationally challenging due to optimization of a non-smooth loss function. Previous work has shown that rank-based estimators may be written as solutions to linear programming (LP) problems. However, the size of the LP problem is O(n ²+p) subject to n ² linear constraints, where n denotes sample size and p denotes the dimension of parameters. As n and/or p increases, the feasibility of such solution in practice becomes questionable. Among data mining and statistical learning enthusiasts, there is interest in extending ordinary regression coefficient estimators for low-dimensions into high-dimensional data mining tools through regularization. Applying this recipe to rank-based coefficient estimators leads to formidable optimization problems which may be avoided through smooth approximations to non-smooth functions. We review smooth approximations and quasi-Newton methods for rank-based estimation in AFT models. The computational cost of our method is substantially smaller than the corresponding LP problem and can be applied to small- or large-scale problems similarly. The algorithm described here allows one to couple rank-based estimation for censored data with virtually any regularization and is exemplified through four case studies.     

Correlation Coefficient Revisited

Elementary approaches to prove basic properties of the correlation coefficient are of pedagogical interest. Besides posing another proof, this article gives variations of the proofs already existing in the statistical literature     

Multiplicative Adjustment Method for Semiparametric Regression with Mixing Dependent Data

In this article, we extend a semiparametric regression estimator with multiplicative adjustment to time series context. The asymptotic theory and results from a simulation study are discussed. Theoretical results and numerical comparison show that, in the time series case, the semiparametric estimator is better than the traditional local polynomial estimator in a wide neighbourhood around the true regression function.     

Approximating discrete multivariate distributions prom known moments

To approximate the joint distribution of the two-colony stepping-stone model, a finite mixture approach is proposed for constructing discrete multi-variate distributions. This approach generahzes the classic method of linear combinations of independent variables. The stepping-stone model is approximated through matching known moments. Numerical examples from entomology are given. Comparisons are made with the work by Wehrly et al (1993).     

《宋史·瞎征传》笺证

瞎征为阿里骨之子,系北宋时期河湟吐蕃政权的第四任赞普.瞎征继位之时,唃厮哆政权内部面临着一浪高过一浪的内讧和反叛,外部则遭受到北宋政府发动的新一轮的进攻,内外交困.在这种情况之下,瞎征继位三年就不得不结束了自己的政治生涯.瞎征执政最终使唃厮啰政权彻底地走向衰落.     

杭州汽轮机厂计算机集成制造系统——HTW-CIMS

本文介绍了杭州汽轮机厂计算机集成制造系统HTW-CIMS的总体方案以及开发和应用该系统时遵循的技术路线。     

从教师专业实践能力发展反观高校与幼儿园的双向融合

当前,打通高校教师理论研究与基层一线教师专业发展瓶颈是总体提升我国教育质量的高效之举,教育的实践性本质也决定了高校教师的理论研究与一线教师的教育实践必须坚持双向融合的道路。实现校园双向融合既要积极构建教师发展共同体,还要积极探索有深度和广度的教研机制,并搭建开放、多元、创新的资源平台。     

网络视频实时在线评论用户认可度的影响因素

目前,网络视频实时在线评论激增带来的信息爆炸,使实时在线评论信息管理成为重要议题。以往对实时在线评论的研究多集中于个人行为结果方面,而缺乏对实时在线评论信息管理问题的关注;同时,现有视频网站对实时在线评论信息的管理也存在诸多局限。以爱奇艺视频网站热度榜中的电影为样本,基于信息质量维度框架并采用Tobit模型对实时在线评论用户认可度的影响因素进行研究,结果发现：实时在线评论的新颖性、相似性、情感倾向性和信息丰富性,均对实时在线评论的用户认可度存在显著影响,其中新颖性和相似性影响作用最大。视频网站可以据此进一步强化实时在线评论管理,改善用户的体验,保持用户的粘性。