Rényi entropy of m-generalized order statistics

Abstract

Characterizing relations via Rényi entropy of m-generalized order statistics are considered along with examples and related stochastic orderings. Previous results for common order statistics are included.     

Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp-optimization

The conditional tail expectation (CTE) is an indicator of tail behavior that takes into account both the frequency and magnitude of a tail event. However, the asymptotic normality of its empirical estimator requires that the underlying distribution possess a finite variance; this can be a strong restriction in actuarial and financial applications. A valuable alternative is the median shortfall (MS), although it only gives information about the frequency of a tail event. We construct a class of tail L^p-medians encompassing the MS and CTE. For p in (1,2), a tail L^p-median depends on both the frequency and magnitude of tail events, and its empirical estimator is, within the range of the data, asymptotically normal under a condition weaker than a finite variance. We extrapolate this estimator and another technique to extreme levels using the heavy-tailed framework. The estimators are showcased on a simulation study and on real fire insurance data.     

Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.     

Computing maximum likelihood estimates from type II doubly censored exponential data

It is well-known that, under Type II double censoring, the maximum likelihood (ML) estimators of the location and scale parameters, θ and δ, of a twoparameter exponential distribution are linear functions of the order statistics. In contrast, when θ is known, theML estimator of δ does not admit a closed form expression. It is shown, however, that theML estimator of the scale parameter exists and is unique. Moreover, it has good large-sample properties. In addition, sharp lower and upper bounds for this estimator are provided, which can serve as starting points for iterative interpolation methods such as regula falsi. Explicit expressions for the expected Fisher information and Cramér-Rao lower bound are also derived. In the Bayesian context, assuming an inverted gamma prior on δ, the uniqueness, boundedness and asymptotics of the highest posterior density estimator of δ can be deduced in a similar way. Finally, an illustrative example is included.     

非统计专业统计学教育刍议

针对非统计专业统计学教育目前存在的问题,阐述了提高对统计学重要性的认识是保证统计教学效果的前提;统计学教学环节的改革是提高教学效果的重要保证;统计教师的业务素质是提高统计教学效果的关键。     

基于复杂适应系统的作战理论哲学反思

传统的作战理论与方法已经不能适应像现代信息化战争系统这类充满“活”的个体和变化因素的复杂系统，需要进行理论创新。而复杂适应系统理论是当代系统科学的一个新发展。有望成为创新作战理论的突破口。本文在分析比较作战系统的基础上，认为作战系统实质是复杂的适应系统，作战系统内的作战双方都力图以增强自身的适应性和复杂性，削弱对方的适应性和复杂性取得作战的胜利。     

大学研究院管理体制与运行机制创新研究

创新大学研究院管理体制和运行机制，对于推动大学知识和科技创新、加速科技成果产业化和社会化具有重要作用。为此，大学研究院在管理体制方面要实行理事会领导下的院长负责制和虚实结合的矩阵式组织模式。在运行机制方面应建立稳定有力的政策支持体系和科学民主的战略决策制度，建立以团队合作为主的科研队伍体系，建立以竞争为核心的人员聘用体系，建立科学的业绩考评和高效的人才激励制度，建立长效完备的投资渠道，建立积极有效的成果转化机制。     

协同运输管理运行机制研究

协同运输管理是在CPFR基础上发展起来的一种供应链管理方法。它通过降低供应链中的运输成本、缩短商品交付时间和提高准时交货率,实现供应链价值的最大化。协同运输管理的有效实施,需要建立完善的运行机制,包括动力机制、约束机制、实际行为和惩罚机制,并配备相应的软硬件条件。     

找准自己的位置——对地方院校办学定位和发展战略的思考

构建学习型社会,实现高等教育大众化,地方院校的作用不可忽视,关键是要明确办学定位和发展方向。要不断完善功能,创新办学模式,扬长避短,特色发展;坚持以服务为宗旨,发挥本土优势,多层次宽领域办学,全方位多样化发展;以市场和就业为导向,调整专业结构,以就业带动招生,促进学校可持续发展;充分利用外部资源,加强校校合作,与企业和用人单位建立战略伙伴关系,借势发展;走内涵发展为主的路子,不断深化改革,增强办学活力,实质发展。