Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

论诉讼中的"二元"证明标准

诉讼中的证明标准问题是证据法学研究中的一个重要问题.近年来,一些学者从比较研究的角度提出在我国证据法中移植英美法上的"二元"证明标准.本文在理论和实践两个层次分析的基础上提出反驳理由,并指出,这种移植既无必要性,也缺乏可操作性.     

概率准则下的两期投资决策问题

概率准则是以期望收益率为导向的, 是某些情况下投资者的投资决策准则, 它具有一定 的现实指导意义. 在一般情况下, 证券收益率在不同时期的概率分布会不同而且相关. 因此, 提 出了一类概率准则下的两期投资决策问题, 建立了其数学模型. 对于证券收益率为连续及离散 型随机变量这两种情况分别进行了讨论, 给出了求解最优策略的方法及投资决策的步骤, 并举 例予以了说明.     

基于遗传算法的概率准则组合证券模拟求解

针对概率准则意义下的组合证券投资模型,采用随机模拟技术和遗传算法相结合的思 想, 设计出求解算法, 并用Matlab 语言实现. 求解算法适用于证券收益率服从任意分布的情 况, 甚至不考虑证券收益率分布, 用实际数据进行模拟和优化. 实例证明, 该算法有很好的 收敛性及较高的计算效率.     

两类广义卷积

把定义在半直线Ｒ＋＝［０，∞）上概率测度的广义卷积推广到了紧空间Ｒ＋＝［０，∞］上．讨论了与连续性、单位元有关的性质．根据单位元的可能性，导出了两类广义卷积，研究了它们之间的关系．比较了本文所用的推广方法与Ｕｒｂａｎｉｋ的推广方法之间的联系．     

Asymptotic properties of a generalized regression-type predictor of a finite population variance in probability sampling

A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design-unbiased (ADU) and asymptotically design-consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression-type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design-unbiased estimator when the superpopulation model is correct. The generalized regression-type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.     

Fusion of Intelligence Information: A Bayesian Approach

The attack that occurred on September 11, 2001 was, in the end, the result of a failure to detect and prevent the terrorist operations that hit the United States. The U.S. government thus faces at this time the daunting tasks of first, drastically increasing its ability to obtain and interpret different types of signals of impending terrorist attacks with sufficient lead time and accuracy, and second, improving its ability to react effectively. One of the main challenges is the fusion of information, from different sources (U.S. or foreign), and of different types (electronic signals, human intelligence. etc.). Fusion thus involves two very distinct and separate issues: communications, i.e., ensuring that the different U.S. and foreign intelligence agencies communicate all relevant and accurate information in a timely fashion and, perhaps more difficult, merging the content of signals, some "sharp" and some "fuzzy," some dependent and some independent into useful information. The focus of this article is on the latter issue, and on the use of the results. In this article, I present a classic probabilistic Bayesian model sometimes used in engineering risk analysis, which can be helpful in the fusion of information because it allows computation of the posterior probability of an event given its prior probability (before the signal is observed) and the quality of the signal characterized by the probabilities of false positive and false negative. Experience suggests that the nature of these errors has been sometimes misunderstood; therefore, I discuss the validity of several possible definitions.     

论科学证据的两种概率解释

在科学证据研究中,存在＂正相关＂和＂高概率＂两种科学证据的概率定义。从这两个定义出发,能够比较清晰直观地解释科学证据的许多相关问题,如科学证据中的＂比较级＂和＂可接受性＂等。然而,两个定义自身存在着不可忽视的缺陷：＂正相关＂不是证据的充要条件,＂高概率＂也不足以使证据之为证据。     

Confidence intervals,significance values,maximum likelihood estimates,etc. sharpened into Occam’s razors

Abstract

Confidence sets, p values, maximum likelihood estimates, and other results of non-Bayesian statistical methods may be adjusted to favor sampling distributions that are simple compared to others in the parametric family. The adjustments are derived from a prior likelihood function previously used to adjust posterior distributions.     

新巴塞尔协议中信用风险管理IRB法解析

为研究商业银行的信用风险管理问题,对新巴塞尔协议中信用风险管理IRB法进行系统分析,概括其基本框架,并通过详细解析其设定的4种风险要素函数来解释目前我国商业银行在运用各种信用风险管理模型时存在“黑箱”的原因,同时,探讨该方法与各种信用风险管理模型的兼容关系,通过解析信用风险管理模型中各种风险要素函数设定的内在逻辑关系,总结目前我国商业银行应用IRB法时存在的各种数据估计上的困难,从而得出应对各种困难的对策。