基于序关系法的飞行员安全行为评价指标权重系数的确定

飞行员操作行为的表现直接影响民航飞行安全.通过对飞行员不安全行为事故致因数据分析,运用德尔菲(Delphi)调查方法,从安全素养、心理因素、飞行技术及生理因素4个方面确定影响飞行员安全行为的指标体系构成.以飞行员行为评价实例,运用序关系法并结合专家调查确定各评价指标的权重系数,分析了职业安全意识、飞行情景意识、特情处理能力以及机组资源管理能力等17个指标对飞行安全行为的显著性影响程度,结论为飞行员安全行为风险分析提供参考.     

基于Delphi-可拓理论的大型酒店消防安全评价研究

针对大型酒店消防管理的复杂性,综合考虑高层建筑空间火灾发生及消防救援的特点,建立全面多系统的消防安全综合评价指标体系.采用Delphi法将定性指标量化,引入物元可拓理论分析法构建综合可拓评价模型,得到各准则层及系统整体的风险等级和偏离所属等级的趋向程度.以长春市某大型酒店为例,验证了该体系的科学性和可行性.     

手机广告链中移动运营商的努力动机研究与仿真

建立了由单手机广告主、手机广告代理商和移动运营商构成的三级广告产业链之间的基于协作的收益共享契约模型,分析了在以移动运营商为主导的手机广告链中,移动运营商的努力动机受收益共享和努力成本补偿参数的影响.采用量化分析和模拟仿真结合的方法,得出契约参数只有在满足一定条件下,移动运营商所做出的最优努力水平投入决策,将使得整个手机产业链收益最大化,实现多方共赢.     

Skewness Term-Structure Tests

In this paper, we conduct skewness term-structure tests to check whether the temporal structure of risk-neutral skewness is consistent with rational expectations. Because risk-neutral skewness is substantially mean reverting, skewness shocks should decay quickly and risk-neutral skewness of more distant option should display the rationally expected smoothing behaviour. Using an equilibrium asset and option-pricing model in a production economy under jump diffusion with stochastic jump intensity, we derive this elasticity analytically. In an empirical application of the model using more than 20 years of data on S&P500 index options, we find that this elasticity turns out to be different than suggested under rational expectations – smaller on the short end (underreaction) and larger on the long end (overreaction) of the ‘skewness curve’.     

A note on taxation,incentives and risk sharing

The necessary and sufficient conditions for a tax system to guarantee the consistency of private and public evaluation of risky projects are derived. A stochastic dominance approach is used.     

Multiple dependent state sampling plans for lot acceptance based on measurement data

This paper proposes a multiple dependent (or deferred) state sampling plan by variables for the inspection of normally distributed quality characteristics. The decision upon the acceptance of the lot is based on the states of the preceding lots (dependent state plan) or on the states of the forthcoming lots (deferred state plan). The lot acceptance probability is derived and the two-point approach to determining the plan parameters is described. The advantages of this new variables plan over conventional sampling plans are discussed. Tables are constructed for the selection of parameters of this plan under the specific values of the producer’s and consumer’s risks, indexed by acceptable quality level and limiting quality level, when the standard deviation is known or unknown.     

Improved estimators for the GMANOVA problem with application to Monte Carlo simulation

The problem of finding classes of estimators which improve upon the usual (e.g., ML, LS) estimator of the parameter matrix in the GMANOVA model under (matrix) quadratic loss is considered. Classes of improved estimators are obtained via combining integration-by-parts methods for normal and Wishart distributions. Also considered is the application of control variates to achieve better efficiency in multipopulation multivariate simulation studies.     

Ruin Probability in a Semi-Markov Risk Model with Constant Interest Force and Heavy-Tailed Claims

In the present paper, we consider a kind of semi-Markov risk model (SMRM) with constant interest force and heavy-tailed claims, in which the claim rates and sizes are conditionally independent, both fluctuating according to the state of the risk business. First, we derive a matrix integro-differential equation satisfied by the survival probabilities. Second, we analyze the asymptotic behaviors of ruin probabilities in a two-state SMRM with special claim amounts. It is shown that the asymptotic behaviors of ruin probabilities depend only on the state 2 with heavy-tailed claim amounts, not on the state 1 with exponential claim sizes.     

Bayesian networks applied to credit scoring

Email: kchang{at}gmu.eduEmail: RobertFung{at}Fairlsaac.comEmail: alan.lucas{at}hotmail.com^¶Email: BobOliver{at}Fairlsaac.com^||Email: NShikaloff{at}Fairlsaac.com The objectives of this paper are to apply the theory and numericalalgorithms of Bayesian networks to risk scoring, and comparethe results with traditional methods for computing scores andposterior predictions of performance variables. Model identification,inference, and prediction of random variables using Bayesiannetworks have been successfully applied in a number of areas,including medical diagnosis, equipment failure, informationretrieval, rare-event prediction, and pattern recognition. Theability to graphically represent conditional dependencies andindependencies among random variables may also be useful incredit scoring. Although several papers have already appearedin the literature which use graphical models for model identification,as far as we know there have been no explicit experimental resultsthat compare a traditionally computed risk score with predictionsbased on Bayesian learning algorithms. In this paper, we examine a database of credit-card applicantsand attempt to ‘learn’ the graphical structure ofthe characteristics or variables that make up the database.We identify representative Bayesian networks in a developmentsample as well as the associated Markov blankets and cliquestructures within the Markov blanket. Once we obtain the structureof the underlying conditional independencies, we are able toestimate the probabilities of each node conditional on its directpredecessor node(s). We then calculate the posterior probabilitiesand scores of a performance variable for the development sample.Finally, we calculate the receiver operating characteristic(ROC) curves and relative profitability of scorecards basedon these identifications. The results of the different modelsand methods are compared with both development and validationsamples. Finally, we report on a statistical entropy calculationthat measures the degree to which cliques identified in theBayesian network are independent of one another.     

Optimal risk transfer for agents with germs

We introduce a new class of risk measures called generalized entropic risk measures (GERMS) that allow economic agents to have different attitudes towards different sources of risk. We formulate the problem of optimal risk transfer in terms of these risk measures and characterize the optimal transfer contract. The optimal contract involves what we call intertemporal source-dependent quotient sharing, where agents linearly share changes in the aggregate risk reserve that occur in response to shocks to the system over time, with scaling coefficients that depend on the attitudes of each agent towards the source of risk causing the shock. Generalized entropic risk measures are not dilations of a common base risk measure, so our results extend the class of risk measures for which explicit characterizations of the optimal transfer contract can be found.