A Note on “Sourcing Decisions with Stochastic Supplier Reliability and Stochastic Demand”

This note complements the study of Burke, Carillo, and Vakharia ( 2009 hereafter “BCV”) which analyzes a class of single‐product multisourcing problems under stochastic demand and random yields. The purpose is twofold. First, we prove that the objective function used by these authors is only a lower bound for the expected profit for which we provide the correct expression. Second, we show on some of the numerical instances provided in BCV's study that the structure and the performance of the BCV ordering policy may be substantially different from the optimal ordering policy. We conclude by giving general qualitative insights characterizing suboptimality of the BCV solution.     

基于羊群行为的挤兑机理评述

以挤兑羊群行为发生机理和防范措施为主线,对内生特性、太阳黑子、经济基础恶化、信息等挤兑机理进行研究,以及对暂停支付、存款保险、流动性支持、信息披露、政府管制等防范措施进行了分析。结论表明,内生特性及经济恶化理论不能解释挤兑发生的随机性、非理性,太阳黑子过于神秘,而信息挤兑机理克服了上述缺陷;暂停支付是低效的,存款保险会产生道德风险,在流动性支持下的银行更有动机进行过度风险投资,信息披露能防止挤兑发生。     

两类相关的双变量Poisson风险模型的破产概率

经典的风险模型是描述单一险种的风险过程,随着保险公司业务规模的不断扩大,讨论多险种风险过程的破产问题显得越来越必要了。本文研究了两类相关的双变量Poisson风险模型的破产概率问题,并得到了它的破产概率计算公式。     

多角度分析概率问题

从不同的角度来观察随机试验的样本空间 ,则基本随机事件的个数和所求事件的有利场合数都会发生变化 .进一步加深学生对概率论基本概念的理解 ,提高学生分析问题解决问题的能力 .     

马克思主义理论创新的新成果新境界

以江泽民同志为核心的党的第三代领导集体从经济、政治、党建、文化、民族统战、对外开放、祖国统一等各个方面对邓小平理论的创新和发展 ,尤其“三个代表”重要思想是马克思主义理论创新的新成果新境界 ,是新时期加强和改进党的建设、推进我国社会主义制度自我完善和发展的强大理论武器 ,标志着我们党又一次成功地将马克思主义基本原理同中国改革开放实际相结合 ,实现了马克思主义中国化进程中的又一次历史性飞跃     

Monetary policy,trade-offs and the transmission of UK Monetary Policy

We provide a new method to model changes in monetary policy of the Bank of England (BoE) as well as the impact of these changes on UK economy. This is important as central bankers have widened the range of instruments in their monetary policy toolbox. Specifically, we estimate a proxy for the monetary policy stance and then analyse a time-varying parameter vector autoregressive with stochastic volatility model to explain the BoE’s trade-offs when making policy decisions and as well as to demonstrate dynamic impacts of monetary policy on inflation and economic growth. The empirical results show that our estimated monetary policy proxy is better at capturing the BoE’s policy when the interest rate lower bound becomes binding.     

Shape-constrained Gaussian process regression for surface reconstruction and multimodal,non-rigid image registration

We present a new statistical framework for landmark ?>curve-based image registration and surface reconstruction. The proposed method first elastically aligns geometric features (continuous, parameterized curves) to compute local deformations, and then uses a Gaussian random field model to estimate the full deformation vector field as a spatial stochastic process on the entire surface or image domain. The statistical estimation is performed using two different methods: maximum likelihood and Bayesian inference via Markov Chain Monte Carlo sampling. The resulting deformations accurately match corresponding curve regions while also being sufficiently smooth over the entire domain. We present several qualitative and quantitative evaluations of the proposed method on both synthetic and real data. We apply our approach to two different tasks on real data: (1) multimodal medical image registration, and (2) anatomical and pottery surface reconstruction.     

An efficient two-stage randomized response model under stratified random sampling

A two-stage randomized response model is extended to stratified random sampling in order to find out more efficient estimators of proportions built from sensitive questions, which respondents may not answer truthfully, in a population divided into homogeneous subgroups. In each subgroup, the respondents who have not answered the sensitive question in the first stage are requested in the second stage to either answer the sensitive question (second attempt then) or to draw a card indicating “yes” or “no”. In the latter case, they are required to report the outcome. Such extension provides a more efficient estimator of the proportion of the population having a given sensitive attribute than its counterpart in simple random sampling. The extended two-stage randomized response model is more efficient than the stratified randomized response model, where respondents must answer the sensitive question either in the first or in the second stage. Moreover, it increases the respondents’ cooperation. When strata weights are unknown, they are estimated by the double sampling method.     

Adaptive kernel scaling support vector machine with application to a prostate cancer image study

The support vector machine (SVM) is a popularly used classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged with incorporation of observation imbalance. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. The resulting algorithm can efficiently improve the classification accuracy, which is confirmed by intensive numerical studies as well as a real prostate cancer imaging data application.