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.     

脊波导宽频带电磁参数测试技术

针对高损耗介质材料电磁参数的宽频带测试问题,提出了利用脊波导进行测试的新方法,建立了电磁参数测试系统,并采用TRL技术进行了系统校准。该方法仅用3个波段的脊波导即可覆盖2.0～18.0GHz宽频带范围内的电磁参数测试,具有频带宽、体积小、测试精确度高等优点。     

论后印刷时代话语中图像与文字的关系

当代图像与文字的关系体现为“超媒体”和“互动电视网络系统”两种形式。传统符号学以语言为本体论基础来处理两者之间的关系在认识论上是误导，符号的本质也被还原为符号现象。图像与文字，由于各自的不同“给予”，具有不同的认识论要求。认知的中介本质、符号的非中立性以及符号体系必须拥有的三大元功能不仅为区别图像与文字奠定了认识论基础，而且也为探讨它们之间的关系提供了可能性，同时，更是理解虚拟现实的有效途径。     

甲骨文否定词研究

甲骨文中的否定词比较丰富,它们的用法也不完全相同。研究这些否定词的用法,不仅丰富汉语史的内容,而且也看到,秦汉时期汉语中某些否定词的用法,早在3000年前的殷商时代就已形成,说明汉语的历史的确是悠久而又丰富的。     

蒙古语近音近义词刍议

以往对蒙古语近义词的研究 ,只从词义之间的关系出发 ,重点放在解释近义词的意义差别上 ,却很少涉及它的形式结构。虽然蒙古语绝大多数近义词的语音形式相互间毫无关系 ,但是 ,有些近义词的构成 ,具有一定的语音学和形态学基础。通过语音手段构成的近义词是由同一词分化而产生 ;通过形态手段构成的近义词是由同根词派生出来的     

无理数e在电路基本理论中出现的物理意义

对无理数e在电路分析中出现的几个典型例子作了分析讨论，明确指出：由1／e决定的特征常数可以在时间分布上和空间分布上，把一个按指数规律非均匀变化的问题，用一个等效的均匀变化的问题来表示；可以反映初始变化率与最终稳定值之间的联系；可以更深刻地反映某些物理过程的本质特征。     

非英语专业学生英语学习策略使用频率的研究

本调查以内蒙古工业大学非英语专业二年级的109名学生为样本,采取观察和问卷形式,考察非英语专业学生使用何种学习策略以及学习策略的使用频率.结果表明六种学习策略中,非英语专业学生最常用的策略是补偿策略,最不常用的策略是记忆策略.最后,作者指出本文的教学实践意义:即学习策略的培训应融于课堂教学中,其应用应体现于课堂教学活动中     

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.