Kernel matrix learning with a general regularized risk functional criterion

Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.

Wind Power System Risk Assessment Based on Fuzzy Clustering and Copula Function Modeling

According to the characteristics of the correlation of multiple wind farm output, this paper put forwards a modeling method based on fuzzy c-means clustering and the copula function, and correlation wind farms are inserted into IEEE-RTS79 reliability system for risk assessment. By the probabilistic load flow calculated by Monte Carlo simulation method, the probability of the accident is derived, and bus voltage and branch power flow overload risk index are defined in this paper. The results show that this method can realize the modeling of the correlation of wind power output, and the risk index can identify the weakness of the system, which can provide reference for the operation and maintenance personnel.

Risk evaluation of natural disasters based on connection function

There are many uncertain factors,such as stochastic,fuzzy and gray information in the risk analysis on natural hazard.The set pair analysis(SPA)deals effectively with the various uncertain factors contributing to evaluation of the risk level of natural disasters.The evaluation indicators and standards of natural disasters risk are analyzed by identity-discrepancycontrary(IDC).The result,the connection numbers,still has uncertainty information.Thus,yielding the risk evaluation model of natural disasters based on connection function,which construct a set pair relation between the indicators of connection numbers and comprehensive evaluation standards,and describe uncertainty of the connection numbers by using connection function.The study showed that the proposed model takes into account only the uncertainty of risk evaluation indicator identification on natural disasters,also the uncertainty of result connection numbers.This approach gives full consideration to the uncertainty of systematic evaluation process along with the actual meaning of comprehensive evaluation functions.Therefore,this means it is able to reduce the uncertainty of final evaluation results and improve the accuracy and reasonability of evaluation results.This model is capable of reflecting actual situation of the risk evaluation on natural disaster affected by various uncertain factors and has a promotional value in the natural disaster risk assessment.

ON THE EXPECTED DISCOUNTED PENALTY FUNCTION IN A MARKOV-DEPENDENT RISK MODEL WITH CONSTANT DIVIDEND BARRIER

This article considers a Markov-dependent risk model with a constant dividend barrier. A system of integro-differential equations with boundary conditions satisfied by the expected discounted penalty function, with given initial environment state, is derived and solved. Explicit formulas for the discounted penalty function are obtained when the initial surplus is zero or when all the claim amount distributions are from rational family. In two state model, numerical illustrations with exponential claim amounts are given.

NONPARAMETRIC ESTIMATION OF BIVARIATE SURVIVAL FUNCTION UNDER THE COMPETING RISKS CASE

Estimation of the bivariate survival function under the competing risks caseis considered.We give an explicit formula for the estimator from a decomposition of thebivariate survival function based on competing risks,which is almost sure consistent.

Association of Fibrin Monomer Polymerization Function, Cerebrovascular Risk Factors and Ischemic Cerebrovascular Disease in Old People

In order to investigate the association of fibrin monomer polymerization function (FMPF) with traditional cerebrovascular risk factors and ischemic cerebrovascular disease in old people. 1∶1 paired case-control comparative study was performed for FMPF and traditional cerebrovascular risk factors on 110 cases of old ischemic cerebrovascular disease and 110 controls matched on age, sex and living condition. The results showed that cerebrovascular risk factors were more prevalent in case group than in control group. In the case group, FMPF was significantly higher than in control group. There was a significant positive correlation between hypertension and fibrin monomer polymerization velocity (FMPV), hypertension and fibrinogen (Fbg), alcohol consumption and Fbg, but no significant correlation between diabetic mellitus, smoking and FMPF was found. Among the parameters of blood lipids, there were significant positive correlations between total cholesterol (TC) and parameters of FMPF to varying degrees, triglycerides (TG) and FMPV, TG and Fbg. Our results also showed there were significant linear trends between TC and FMPV (P<0. 001), TC and Fbg (P=0. 0087), TG and FMPV/Amax (maximum absorbance)(P=0. 0143) respectively. Multiple logistic regression analysis revealed that FMPF in case group remained significantly higher than control group after adjustment of all risk factors that were significant in univariate analysis. It was concluded that there is a possible pathophysiological link between FMPF and cerebrovascular risk factors. An elevated FMPF is associated with ischemic cerebrovascular disease and an independent risk factor of this disease. In old people, detection of FMPF might be a useful screening to identify individuals at increased cerebrothrombotic risk.

Randomized Objective Function Linear Programming in Risk Management

The traditional linear programming model is deterministic. The way that uncertainty is handled is to compute the range of optimality. After the optimal solution is obtained, typically by the simplex method, one considers the effect of varying each objective function coefficient, one at a time. This yields the range of optimality within which the decision variables remain constant. This sensitivity analysis is useful for helping the analyst get a sense for the problem. However, it is unrealistic because objective function coefficients tend not to stand still. They are typically profit contributions from products sold and are subject to randomly varying selling prices. In this paper, a realistic linear program is created for simultaneously randomizing the coefficients from any probability distribution. Furthermore, we present a novel approach for designing a copula of random objective function coefficients according to a specified rank correlation. The corresponding distribution of objective function values is created. This distribution is examined directly for central tendency, spread, skewness and extreme values for the purpose of risk analysis. This enables risk analysis and business analytics, emerging topics in education and preparation for the knowledge economy.

老年特发性正常压力脑积水患者的姿势稳定性特征

背景:姿势控制能力受损是特发性正常压力脑积水患者发生跌倒、引发继发性损害的重要危险因素,现有研究大多是对患者直线行走时的步态参数进行分析,鲜有对患者在静态与动态活动中的姿势稳定性特征进行分析。目的:分析老年特发性正常压力脑积水患者的姿势稳定性特征。方法:选择2022年9月至2023年2月在上海市复旦大学附属华东医院神经外科临床诊断为特发性正常压力脑积水的患者22例作为患者组,同时选择健康陪诊家属18名作为健康对照组。分别采用计时起立-行走测试、多方向性伸展测试、Berg平衡量表和静态平衡功能测试(反应时间、移动速度、方向控制、最大偏移距离、端点行程)评估受试者的姿势稳定性特征。结果与结论:①患者组完成计时起立-行走测试所需时间及静态平衡功能测试的反应时间均长于健康对照组(P<0.05),前、后、左、右方向上的伸展测试手臂伸展距离均小于健康对照组(P<0.05),Berg平衡量表得分低于健康对照组(P<0.05),静态平衡功能测试的移动速度、方向控制、最大偏移距离和端点行程均小于健康对照组(P<0.05)。②结果提示,特发性正常压力脑积水患者表现出整体的姿势控制能力障碍,反应能力与执行能力受损使得这类患者在面对来自内部或外部的干扰时无法做出及时、准确的运动反应而出现姿势不稳,增加了发生跌倒的风险。

基于风险偏好的区间毕达哥拉斯犹豫模糊得分函数及其多属性决策

针对属性权重已知、评价信息为区间毕达哥拉斯犹豫模糊数的多属性决策问题,对区间毕达哥拉斯犹豫模糊集的得分函数进行深入分析,考虑决策者的主观态度对决策结果的影响,提出一种基于风险偏好因子的新得分函数.首先,介绍区间毕达哥拉斯犹豫模糊数的基本定义和相关运算,考虑决策者在面临风险时的不同主观价值感受,进而通过引入风险偏好因子提出新的得分函数和排序准则.其次,给出新得分函数的取值范围、最值和单调性等性质并进行证明.最后,基于该得分函数给出一种全新的TOPSIS多属性决策方法,并通过解决实际问题检验该方法的可行性、实用性和有效性.区间毕达哥拉斯犹豫模糊数得分函数的改进丰富了多属性决策理论.

Study on risk measurement about ammunition-rocket system

Modern ammunition-rocket system is a complicated multidisciplinary system. During its development, undetermined factors will bring many risks. This paper elaborates the importance of risk analysis approach to ammunition-rocket system development and analyses various methods of risk analysis and estimation. Combined with practical situation of weapon system development, the risk measurement function with characteristics of risk preference is given provided that the risk preference characteristic of behavior maker is risk neutral of fixed constant. The development risk analysis based on risk measurement function enables effective risk decision to be made on the basis of quantified risk. Taking anti-helicopter intelligent mine warhead as an example, the paper verifies the efficiency of the method and shows that it has a scientific and practical value.

A Bayesian Network Approach for Offshore Risk Analysis Through Linguistic Variables

This paper presents a new approach for offshore risk analysis that is capable of dealing with linguistic probabilities in Bayesian networks （ BNs）. In this paper, linguistic probabilities are used to describe occurrence likelihood of hazardous events that may cause possible accidents in offshore operations. In order to use fuzzy information, an f-weighted valuation function is proposed to transform linguistic judgements into crisp probability distributions which can be easily put into a BN to model causal relationships among risk factors. The use of linguistic variables makes it easier for human experts to express their knowledge, and the transformation of linguistic judgements into crisp probabilities can significantly save the cost of computation, modifying and maintaining a BN model. The flexibility of the method allows for multiple forms of information to be used to quantify model relationships, including formally assessed expert opinion when quantitative data are lacking, or when only qualitative or vague statements can be made. The model is a modular representation of uncertain knowledge caused due to randomness, vagueness and ignorance. This makes the risk analysis of offshore engineering systems more functional and easier in many assessment contexts. Specifically, the proposed f-weighted valuation function takes into account not only the dominating values, but also the a-level values that are ignored by conventional valuation methods. A case study of the collision risk between a Floating Production, Storage and Off-loading （FPSO） unit and the anthorised vessels due to human elements during operation is used to illustrate the application of the proposed model.

Nelson-Aalen and Kaplan-Meier Estimators in Competing Risks

In this paper, stochastic processes developed by Aalen [1]?[2] are adapted to the Nelson-Aalen and Kaplan-Meier?[3] estimators in a context of competing risks. We focus only on the probability distributions of complete downtime individuals whose causes are known and which bring us to consider a partition of individuals into sub-groups for each cause. We then study the asymptotic properties of nonparametric estimators obtained.

Markov process functionals in finance and insurance

The Markov property of Markov process functionals which are frequently used in economy, finance, engineering and statistic analysis is studied. The conditions to judge Markov property of some important Markov process functionals are presented, the following conclusions are obtained： the multidimensional process with independent increments is a multidimensional Markov process; the functional in the form of path integral of process with independent increments is a Markov process; the surplus process with the doubly stochastic Poisson process is a vector Markov process. The conditions for linear transformation of vector Markov process being still a Markov process are given.

Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming

Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality. © 2014 Chinese Association of Automation.

Model of counterparty risk with geometric attenuation and valuation of CDS

To investigate the impact of microstructure interdependency of a counterparty explicitly, a geometric function is introduced in one firm's default intensity to reflect the attenuation behavior of the impact of its counterparty firm's default. The general joint distribution and marginal distributions of default times are derived by employing the change of measure. The fair premium of a vanilla CDS (credit default swap) is obtained in continuous and discrete contexts, respectively. The swap premium in a discrete context is similar to the accumulated interest during the period between two payment days, and the short rate is the swap rate in a continuous context.

Effects of professional rehabilitation training on the recovery of neurological function in young stroke patients

Young stroke patients have a strong desire to return to the society, but few studies have been conducted on their rehabilitation training items, intensity, and prognosis. We analyzed clinical data of young and middle-aged/older stroke patients hospitalized in the Department of Neurological Rehabilitation, China Rehabilitation Research Center, Capital Medical University, China from February 2014 to May 2015. Results demonstrated that hemorrhagic stroke （59.6%） was the primary stroke type found in the young group, while ischemic stroke （60.0%） was the main type detected in the middle-aged/older group. Compared with older stroke patients, education level and incidence of hyperhomocysteinemia were higher in younger stroke patients, whereas, incidences of hypertension, diabetes, and heart disease were lower. The average length of hospital stay was longer in the young group than in the middle-aged/older group. The main risk factors observed in the young stroke patients were hypertension, drinking, smoking, hyperlipidemia, hyperhomocysteinemia, diabetes, previous history of stroke, and heart disease. The most accepted rehabilitation program consisted of physiotherapy, occupational therapy, speech therapy, acupuncture and moxibustion. Average rehabilitation training time was 2.5 hours/day. Barthel Index and modified Rankin Scale scores were increased at discharge. Six months after discharge, the degree of occupational and economic satisfaction declined, and there were no changes in family life satisfaction. The degrees of other life satisfaction （such as friendship） improved. The degree of disability and functional status improved significantly in young stroke patients after professional rehabilitation, but the number of patients who returned to society within 6 months after stroke was still small.

Dependence Model Selection for Semi-Competing Risks Data

We consider the model selection problem of the dependency between the?terminal event and the non-terminal event under semi-competing risks data. When the relationship between the two events is unspecified, the inference on the non-terminal event is not identifiable. We cannot make inference on the non-terminal event without extra assumptions. Thus, an association model for?semi-competing risks data is necessary, and it is important to select an appropriate dependence model for a data set. We construct the likelihood function for semi-competing risks data to select an appropriate dependence model. From?simulation studies, it shows the performance of the proposed approach is well. Finally, we apply our method to a bone marrow transplant data set.

Dividend Payments with a Hybrid Strategy in the Compound Poisson Risk Model

In this paper, a hybrid dividend strategy in the compound Poisson risk model is considered. In the absence of dividends, the surplus of an insurance company is modelled by a compound Poisson process. Dividends are paid at a constant rate whenever the modified surplus is in a interval;the premium income no longer goes into the surplus but is paid out as dividends whenever the modified surplus exceeds the upper bound of the interval, otherwise no dividends are paid. Integro-differential equations with boundary conditions satisfied by the expected total discounted dividends until ruin are derived;for example, closed-form solutions are given when claims are exponentially distributed. Accordingly, the moments and moment-generating functions of total discounted dividends until ruin are considered. Finally, the Gerber-Shiu function and Laplace transform of the ruin time are discussed.

考虑国际金融风险影响的上证50ETF期权定价研究

近年来如何刻画国际金融风险对中国市场的影响,成为学术界的热门热点之一。已有文献大多集中于研究国际股票市场之间的风险溢出效应,较少关注国际股票市场对中国期权市场的风险外溢效应。本文将标普500ETF走势嵌入上证50ETF的收益率过程,构建IFR_BS模型(BS Model with the Impact of International Financial Risk);然后应用特征函数微扰法和Fourier-Cosine定价方法,推导出该模型下欧式期权的近似解析定价公式。数值实验和实证结果表明:(1)IFR_BS模型可以较好地刻画上证50ETF收益率分布的“尖峰”、“肥尾”和“有偏”等统计特征。(2)考虑国际金融风险溢价的IFR_BS模型下的期权定价公式,可以解决BS模型对短到期期权尤其是短到期深度OTM期权估值不足的问题。

采用三次样条函数拟合蚊媒密度概率分布及其风险评估

目的研究登革热蚊媒MOI风险的概率分布,为更加科学精准开展MOI风险评估提供新方法。方法采用三次样条函数、累积概率分布和Python语言编程对广州市2016—2019年各月MOI监测数据进行拟合分析。结果本研究绘制了广州市1-12月的MOI风险评估概率分布表和分布图。1-12月各月累积概率小于0.01和0.05的MOI临界值分别在7.54~60.12、5.02~34.10之间。该临界值均在1月最低,其次是12月和2月;均在6月份最高,其次是5月和7月。1-12月各月MOI风险等级为传播、暴发和流行的概率分别在5.07%~66.60%、0.25%~36.19%、0.00%~16.80%之间。传播、暴发和流行的风险概率最低值均出现在1月,其次均在2月;最高值分别出现在7月、7月和6月,其次是6月、6月和5月。结论广州市各月的MOI风险概率分布具有显著的季节性消长规律和特征,在开展登革热蚊媒监测评估与控制工作中值得重视。三次样条函数和累积概率分布拟合法为开展MOI风险评估提供了新的分析方法。