The Joint Distribution of the Sum and the Maximum of IID Exponential Random Variables

The authors establish the joint distribution of the sum X and the maximum Y of IID exponential random variables. They derive exact formuli describing the random vector (X, Y), including its joint PDF, CDF, and other characteristics; marginal and conditional distributions; moments and related parameters; and stochastic representations leading to further properties of infinite divisibility and self-decomposability. The authors also discuss parameter estimation and include an example from climatology that illustrates the modeling potential of this new bivariate model.     

冒辟疆和他的三个园子

冒辟疆史称明末四公子之一，本文根据史实说明明末清初易代之际冒氏对当时政治、文化、社会生活的干预与作为。“朴巢”、“水绘园”和“匿峰庐”这三个园子可以说构成他生命中三个时期的缩影。     

Optimal designs for response functions with a downturn

In many toxicological assays, interactions between primary and secondary effects may cause a downturn in mean responses at high doses. In this situation, the typical monotonicity assumption is invalid and may be quite misleading. Prior literature addresses the analysis of response functions with a downturn, but so far as we know, this paper initiates the study of experimental design for this situation. A growth model is combined with a death model to allow for the downturn in mean doses. Several different objective functions are studied. When the number of treatments equals the number of parameters, Fisher information is found to be independent of the model of the treatment means and on the magnitudes of the treatments. In general, A- and D_A-optimal weights for estimating adjacent mean differences are found analytically for a simple model and numerically for a biologically motivated model. Results on c-optimality are also obtained for estimating the peak dose and the EC₅₀ (the treatment with response half way between the control and the peak response on the increasing portion of the response function). Finally, when interest lies only in the increasing portion of the response function, we propose composite D-optimal designs.     

基于极值Copula的投资组合集成风险度量方法

 应用极值的阈值与峰值模型来度量单个资产的风险价值,用两种不同的方法度量了基于Copula函数的沪深指数收益率的相关结构,比较了不同Copula函数下基于沪深指数的二元投资组合集成风险值,结果说明：Gauss Copula函数对沪深指数收益率的相关结构拟合较好,阈值模型的极值Copula能较好的度量投资组合的集成风险值,在高置信度下（0.99以上）,基于Gumble Copula函数的上尾（正收益）集成风险值、基于Clayton Copula函数的下尾（负收益）集成风险值与真实值最为接近。直接加权的方法会高估投资组合的风险,假设沪深指数的收益率服从二元正态分布会低估风险。峰值法的集成风险值误差较大。     

武当山太子坡狮子峰"老君岩"石窟造像考

武当山太子坡狮子峰佛、道摩崖造像群的开凿年代及身份,历来颇有争议,明、清所有版本的<武当山志>均沿用明初武当山玉虚宫提点任自垣断为北宋道教老君造像的说法,然而,根据现存摩崖石刻以及历史背景和大量的文献资料分析,它应为佛教的产物;也非北宋时期的造像,应为唐代作品.任的观点比实际凿刻时间晚400多年,至今讹传近600年.     

Adaptive Isotonic Estimation of the Minimum Effective and Peak Doses in the Presence of Covariates

We consider a problem of estimating the minimum effective and peak doses in the presence of covariates. We propose a sequential strategy for subject assignment that includes an adaptive randomization component to balance the allocation to placebo and active doses with respect to covariates. We conclude that either adjusting for covariates in the model or balancing allocation with respect to covariates is required to avoid bias in the target dose estimation. We also compute optimal allocation to estimate the minimum effective and peak doses in discrete dose space using isotonic regression.     

高峰体验与人格完善——论马斯洛的宗教心理学

美国人本主义心理学的开创者A.H.马斯洛通过需求层次论、自我实现者、高峰体验、高原体验等一系列重要心理学概念,描述了个体的人格完善与宗教信仰及其经验之间的关系。人们注重他的心理学说,却忽视了其中的宗教意蕴。他的宗教心理学有利于我们理解自身,并追求成功和幸福。     

武当山历史地位的变迁

“武当”之名最早出现在《汉书》中,汉末至魏晋隋唐时期,是求仙学道者的栖隐之地。至宋代,道经始将传说中的真武神与武当山联系起来,将武当山附会成真武的出生地和飞升处,为它以后的显荣尊贵打下了基础,入明以后,由于统治者对真武的尊崇,武当山被封为“太岳”、“玄岳”,成为“天下第一名山”。清以后,武当山的地位大为下降,它对周边地区和区域社会的影响,主要通过朝山进香等民俗活动来体现。     

马斯洛高峰体验学说及其对教学的启示

"高峰体验"是马斯洛自我实现理论的延伸,是自我实现心理学的有机组成部分.本文阐述了高峰体验时人的四个特征,即释放性、创新性、审美性和独特性,并将该学说运用到现行课程改革中,分别论述该学说对教学目标、教学过程、教学对象、教学结果的借鉴意义.     

Acknowledgment of the Editorial Assistants,Book Review Editors,and Associate Editors

Since the extensive work by Burns and Mitchell, many economists have interpreted economic fluctuations in terms of business-cycle phases. Given this, we argue that, in addition to usual model-selection criteria currently used in the profession, the adequacy of a univariate macroeconomic time series model should be based on its ability to replicate two important business-cycle features of the U.S. data—duration and amplitude. We propose several checks for whether univariate statistical models generate business-cycle features observed in U.S. gross domestic product (GDP) and find that many popular nonlinear models for the log of real GDP are no better at replicating the duration and amplitude features of the data than a simple ARIMA(1, 1, 0).