R&D项目中止决策的小波网络模式识别

如何科学、合理、有效地识别和中止那些行将失败或没有前途的 R&D项目 ,对于企业成功地进行技术创新、建立和保持竞争优势 ,显然具有十分重要的理论价值和实践意义 .本文根据中止决策的实质及模式识别的基本原理 ,探讨了定量地中止 R&D项目的一般模式识别程序 .同时 ,基于小波基具有很强的自适应数据和函数变化的能力 ,进一步研究给出了一种基于小波网络的 R&D项目中止决策分析模式识别新方法 ,并在最小均方能量准则下 ,采用相应的共轭梯度学习算法求解子波函数线性组合的尺度和时延参数 ,以及神经网络的权值 .本研究不仅克服了现有 R&D项目中止决策分析技术在构造与求解模型方面的某些局限性 ,并且为R&D项目的跟踪管理提供了一种新的理论依据 ,为具体决策单位和政府有关部门对正在实施的 R&D项目作中止决策提供了一种可操作的决策方法和程序     

不对称信息下信息技术服务外包合同设计

本文针对客户企业向IT供应商外包信息技术服务这一背景,考虑IT供应商不仅具有开发信息系统能力的私有信息,且其开发过程中的努力行为对于客户企业不可见的情况,研究了客户企业信息技术服务外包合同设计问题。研究发现,不对称信息下,客户企业可以通过设计最优合同菜单来甄别不同能力的IT供应商,但是此时客户企业需要向高能力IT供应商支付信息租金。同时,客户企业可以通过设计最优合同有效规制高能力IT供应商系统开发过程中的努力行为,但是相比于信息对称的情况,不对称信息下低能力IT供应商会存在努力不足的现象。低能力IT供应商努力不足的现象会降低社会福利和客户企业的期望利润;客户企业向高能力IT供应商支付信息租金的行为也会减少客户企业的期望利润。增加系统调试阶段的调试时间或者选择信息系统故障检出率高的IT供应商,可以降低高能力IT供应商的信息租金,并缓解低能力IT供应商努力不足的现象,进而减少信息不对称给客户企业带来的利润损失。     

Florida Households’ Expected Responses to Hurricane Hazard Mitigation Incentives

This study tested a series of models predicting household expectations of participating in hurricane hazard mitigation incentive programs. Data from 599 households in Florida revealed that mitigation incentive adoption expectations were most strongly and consistently related to hazard intrusiveness and risk perception and, to a lesser extent, worry. Demographic and hazard exposure had indirect effects on mitigation incentive adoption expectations that were mediated by the psychological variables. The results also revealed differences in the factors affecting mitigation incentive adoption expectations for each of five specific incentive programs. Overall, the results suggest that hazard managers are more likely to increase participation in mitigation incentive programs if they provide messages that repeatedly (thus increasing hazard intrusiveness) remind people of the likelihood of severe negative consequences of hurricane impact (thus increasing risk perception).     

Emotions,Trust, and Perceived Risk: Affective and Cognitive Routes to Flood Preparedness Behavior

Despite the prognoses of the effects of global warming (e.g., rising sea levels, increasing river discharges), few international studies have addressed how flood preparedness should be stimulated among private citizens. This article aims to predict Dutch citizens’ flood preparedness intentions by testing a path model, including previous flood hazard experiences, trust in public flood protection, and flood risk perceptions (both affective and cognitive components). Data were collected through questionnaire surveys in two coastal communities (n= 169, n= 244) and in one river area community (n= 658). Causal relations were tested by means of structural equation modeling (SEM). Overall, the results indicate that both cognitive and affective mechanisms influence citizens’ preparedness intentions. First, a higher level of trust reduces citizens’ perceptions of flood likelihood, which in turn hampers their flood preparedness intentions (cognitive route). Second, trust also lessens the amount of dread evoked by flood risk, which in turn impedes flood preparedness intentions (affective route). Moreover, the affective route showed that levels of dread were especially influenced by citizens’ negative and positive emotions related to their previous flood hazard experiences. Negative emotions most often reflected fear and powerlessness, while positive emotions most frequently reflected feelings of solidarity. The results are consistent with the affect heuristic and the historical context of Dutch flood risk management. The great challenge for flood risk management is the accommodation of both cognitive and affective mechanisms in risk communications, especially when most people lack an emotional basis stemming from previous flood hazard events.     

Characterizations based on Rényi entropy of order statistics and record values

Two different distributions may have equal Rényi entropy; thus a distribution cannot be identified by its Rényi entropy. In this paper, we explore properties of the Rényi entropy of order statistics. Several characterizations are established based on the Rényi entropy of order statistics and record values. These include characterizations of a distribution on the basis of the differences between Rényi entropies of sequences of order statistics and the parent distribution.     

Estimation of a Change Point in a Hazard Function Based on Censored Data

The hazard function plays an important role in reliability or survival studies since it describes the instantaneous risk of failure of items at a time point, given that they have not failed before. In some real life applications, abrupt changes in the hazard function are observed due to overhauls, major operations or specific maintenance activities. In such situations it is of interest to detect the location where such a change occurs and estimate the size of the change. In this paper we consider the problem of estimating a single change point in a piecewise constant hazard function when the observed variables are subject to random censoring. We suggest an estimation procedure that is based on certain structural properties and on least squares ideas. A simulation study is carried out to compare the performance of this estimator with two estimators available in the literature: an estimator based on a functional of the Nelson-Aalen estimator and a maximum likelihood estimator. The proposed least squares estimator tums out to be less biased than the other two estimators, but has a larger variance. We illustrate the estimation method on some real data sets.     

Urban tree cover: an ecological perspective

Analysis of urban tree cover is generally limited to inventories of tree structure and composition on public lands. This approach provided valuable information for resource management. However, it does not account for all tree cover within an urban landscape, thus providing insufficient information on ecological patterns and processes. We propose evaluating tree cover for an entire urban area that is based on patch dynamics. Treed patches are classified by their origin, structure, and management intensity. A patch approach enables ecologists to evaluate ecological patterns and processes for the entire urban landscape and to examine how social patterns influence these ecological patterns and processes.     

Improving the Efficiency of the Nelson–Aalen Estimator: the Naive Local Constant Estimator

Abstract.  The Nelson–Aalen estimator is well known to be an asymptotically efficient estimator of the cumulative hazard function, see Andersen et al. ( Statistical models based on counting processes , Springer-Verlag, New York, 1993) among many others. In this paper, we show that the efficiency of the Nelson–Aalen estimator can be considerably improved by using more information in the estimation process than the traditional Nelson–Aalen estimator uses. While our approach results in a biased estimator, the variance improvement is substantial. By optimizing the balance between the bias loss and the variance improvement, we obtain results on the efficiency gain. Several examples for known failure time distributions are used to illustrate these ideas.     

论社会医疗保险中的道德风险及其制度消解

道德风险源于人的机会主义倾向,在社会医疗保险领域中积弊已久,主要表现为需求方的过度消费和供给方的诱导需求,其原因在于第三方支付效应、价格补偿效应和医疗伦理的异化。只有医、患、保三方进行制度创新才能对其有效消解。     

Nonparametric multiple expectile regression via ER-Boost

Expectile regression [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847] is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. Expectile regression at 50% level is the classical conditional mean regression. In many real applications having multiple expectiles at different levels provides a more complete picture of the conditional distribution of the response variable. Multiple linear expectile regression model has been well studied [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847; Efron B. Regression percentiles using asymmetric squared error loss, Stat Sin. 1991;1(93):125.], but it can be too restrictive for many real applications. In this paper, we derive a regression tree-based gradient boosting estimator for nonparametric multiple expectile regression. The new estimator, referred to as ER-Boost, is implemented in an R package erboost publicly available at http://cran.r-project.org/web/packages/erboost/index.html. We use two homoscedastic/heteroscedastic random-function-generator models in simulation to show the high predictive accuracy of ER-Boost. As an application, we apply ER-Boost to analyse North Carolina County crime data. From the nonparametric expectile regression analysis of this dataset, we draw several interesting conclusions that are consistent with the previous study using the economic model of crime. This real data example also provides a good demonstration of some nice features of ER-Boost, such as its ability to handle different types of covariates and its model interpretation tools.