Exact controllability of rational expectations model with multiplicative noise and input delay

This paper considers the rational expectations model with multiplicative noise and input delay,where the system dynamics rely on the conditional expectations of future states.The main contribution is to obtain a sufficient condition for the exact controllability of the rational expectations model.In particular,we derive a sufficient Gramian matrix condition and a rank condition for the delay-free case.The key is the solvability of the backward stochastic difference equations with input delay which is derived from the forward and backward stochastic system.

The Expected Discounted Tax Payments on Dual Risk Model under a Dividend Threshold

In this paper, we consider the dual risk model in which periodic taxation are paid according to a loss-carry-forward system and dividends are paid under a threshold strategy. We give an analytical approach to derive the expression of gδ(u) (i.e. the Laplace transform of the first upper exit time). We discuss the expected discounted tax payments for this model and obtain its corresponding integro-differential equations. Finally, for Erlang (2) inter-innovation distribution, closedform expressions for the expected discounted tax payments are given.

Cybersecurity: A Statistical Predictive Model for the Expected Path Length

The object of this study is to propose a statistical model for predicting the Expected Path Length (expected number of steps the attacker will take, starting from the initial state to compromise the security goal—EPL) in a cyber-attack. The model we developed is based on utilizing vulnerability information along with having host centric attack graph. Utilizing the developed model, one can identify the interaction among the vulnerabilities and individual variables (risk factors) that drive the Expected Path Length. Gaining a better understanding of the relationship between vulnerabilities and their interactions can provide security administrators a better view and an understanding of their security status. In addition, we have also ranked the attributable variables and their contribution in estimating the subject length. Thus, one can utilize the ranking process to take precautions and actions to minimize Expected Path Length.

Impact of cancer diagnosis on life expectancy by area-level socioeconomic groups in New South Wales, Australia: a population-based study

Objective: Improvement in cancer survival over recent decades has not been accompanied by a narrowing of socioeconomic disparities. This study aimed to quantify the loss of life expectancy(LOLE) resulting from a cancer diagnosis and examine disparities in LOLE based on area-level socioeconomic status(SES).Methods: Data were collected for all people between 50 and 89 years of age who were diagnosed with cancer, registered in the NSW Cancer Registry between 2001 and 2019, and underwent mortality follow-up evaluations until December 2020. Flexible parametric survival models were fitted to estimate the LOLE by gender and area-level SES for 12 common cancers.Results: Of 422,680 people with cancer, 24% and 18% lived in the most and least disadvantaged areas, respectively. Patients from the most disadvantaged areas had a significantly greater average LOLE than patients from the least disadvantaged areas for cancers with high survival rates, including prostate [2.9 years(95% CI: 2.5±3.2 years) vs. 1.6 years(95% CI: 1.3±1.9 years)] and breast cancer [1.6 years(95% CI: 1.4±1.8 years) vs. 1.2 years(95% CI: 1.0±1.4 years)]. The highest average LOLE occurred in males residing in the most disadvantaged areas with pancreatic [16.5 years(95% CI: 16.1±16.8 years) vs. 16.2 years(95% CI: 15.7±16.7 years)] and liver cancer [15.5 years(95% CI: 15.0±16.0 years) vs. 14.7 years(95% CI: 14.0±15.5 years)]. Females residing in the least disadvantaged areas with thyroid cancer [0.9 years(95% CI: 0.4±1.4 years) vs. 0.6 years(95% CI: 0.2±1.0 years)] or melanoma [0.9 years(95% CI: 0.8±1.1 years) vs. 0.7 years(95% CI: 0.5±0.8 years)] had the lowest average LOLE.Conclusions: Patients from the most disadvantaged areas had the highest LOLE with SES-based differences greatest for patients diagnosed with cancer at an early stage or cancers with higher survival rates, suggesting the need to prioritise early detection and reduce treatment-related barriers and survivorship challenges to improve life expectancy.

On the Expected Present Value of Total Dividends in a Risk Model with Potentially Delayed Claims

In this paper, we consider a risk model in which two types of individual claims, main claims and by-claims, are defined. Every by-claim is induced by the main claim randomly and may be delayed for one time period with a certain probability. The dividend policy that certain amount of dividends will be paid as long as the surplus is greater than a constant dividend barrier is also introduced into this delayed claims risk model. By means of the probability generating functions, formulae for the expected present value of total dividend payments prior to ruin are obtained for discrete-type individual claims. Explicit expressions for the corresponding results are derived for K n claim amount distributions. Numerical illustrations are also given.

Research on the Construction and Application of Service Quality Evaluation Models for Scenic Spots:Taking Chongqing Ciqikou as an Example

In the current environment of increasingly fierce competition in the tourism industry,service quality has become crucial for enhancing the competitiveness of scenic spots.This paper uses the SERVQUAL model to design a service quality evaluation questionnaire that captures the gap between tourists’expectations and perceptions,using the Ciqikou Scenic Spot as a case study.Data collected from field surveys are used to comprehensively and meticulously evaluate the service quality of the Ciqikou Scenic Spot.The analysis results show that the scenic spot,with its unique folk culture experience and beautiful ecological environment,has certain advantages in terms of service quality.However,significant deficiencies exist in infrastructure and environmental hygiene.Accordingly,targeted improvement suggestions are proposed to further enhance the service quality of the Ciqikou Scenic Spot and meet the increasingly diverse and personalized needs of tourists.This study provides not only a specific service quality improvement strategy for the Ciqikou Scenic Spot but also a valuable reference for other tourist attractions.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

Optimal Risk Sharing for Maxmin Choquet Expected Utility Model

This article analyzes the Pareto optimal allocations,agreeable trades and agreeable bets under the maxmin Choquet expected utility(MCEU)model.We provide several useful characterizations for Pareto optimal allocations for risk averse agents.We derive the formulation descriptions for non-existence agreeable trades or agreeable bets for risk neutral agents.We build some relationships between ex-ante stage and interim stage on agreeable trades or bets when new information arrives.

Depressed Older Patients’ Need for and Expectations of Improved Health Services—An Evaluative Approach to the Chronic Care Model

Depression in later life is an underrepresented yet important research area. The aim of the study was to explore depressed older persons’ need for and expectations of improved health services one year after implementation of the Chronic Care Model (CCM). A qualitative evaluative design was used. Data were collected through individual interviews with older persons living in Norway. The qualitative content analysis revealed two themes: The need to be safeguarded and Expectation of being considered valuable and capable. Evaluation of the improvement in care with focus on the CCM components showed that the most important components for improving the depressed older person’s daily life were: delivery system re-design, self-management support, productive interaction and a well-informed active patient. The findings highlight the need for a health services designed for persons suffering from chronic ill-health, where the CCM could serve as a framework for policy change and support the redesign of the existing healthcare system. We conclude that older persons with depression need attention, especially those who have been suffering for many years. The identified components may have implications for health professionals in the promotion of mental healthcare.

Reduced Life Expectancy Model Analyses of Exposure Time Effects of Endocrine Disruptors to Teleost Fishes Based on Effect Concentration of Hepatic Biomarkers

In this current paper, the exposure time effects on four endocrine disruptors and teleost fishes were evaluated using the reduced life expectancy (RLE) model based on the effect concentration (EC50) of available literature published. The result on the regression analysis over different exposure times has demonstrated that the EC50 of hepatic biomarkers falls with increasing exposure times in a predictable manner. The slopes of the regression equations reflect the strength of the toxic effects on the various teleost fish. The EC50 reduction over time can be interpreted based on the bioconcentration process, which can be used to understand transfer routes of the compounds from water to fish body. RLE model also provides useful information in assessing the toxic effects on fish life expectancy as a result of the occurrence of compounds.

Test of Ordered Multivariate Discrete Selection Model for Average Life Expectancy

At present, there are significant regional differences in average life expectancy among countries in the world. Not only is there a great disparity in average life expectancy, but also the gender difference is positive and negative, and is distributed in a bipolar distribution of “long life in rich countries and short life in poor countries”. This paper analyzes the factors affecting the life grade by using the ordered multivariate discrete selection model and combined with the average life expectancy data of countries all over the world in 2017. The test results show that: 1) The growth of per capita GDP, elderly dependency ratio and the proportion of people using at least basic drinking water services can effectively improve the level of life expectancy;2) The birth rate has an inhibitory effect on the average life expectancy;3) Through model comparison, probit model is more suitable for the analysis of this kind of problems than logit model, and the properties of the obtained model are better.

Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.

生鲜电商平台消费者购菜意愿多元提升路径及适配组态研究

基于南昌市519份调查数据,运用技术接受模型3和期望确认理论模型,采用fsQCA方法对生鲜电商平台消费者购菜意愿进行组态分析,探讨消费者购菜意愿适配组态的要素构成条件及提升策略。结果表明:单一条件变量均不是生鲜电商平台消费者购菜意愿的必要条件,说明变量间可能存在替代作用;存在乐趣性与顾客认可型路径、适应性与情感导向型路径、体验感与顾客评价型路径、个性化与挑战导向型路径、影响力与便利导向型路径等5条产生高购菜意愿的路径;其中,高主观规范和高满意度作为核心条件存在于4条路径中,对生鲜电商平台消费者产生购菜意愿发挥着更普适的作用。基于此,建议整合多元路径,根据组态效应调整购菜意愿提升策略;确保产品质量,提升售后服务,重视满意度的普适性作用;建立良好口碑,发挥社交圈的积极作用,以实现平台发展的良性循环。

投保人高损失区间存在净损失约束的最优保险设计

在不完全保险情形下,投保人通常期望出险后能够获得保险公司足够的赔偿而将自己的实际损失控制在一定的范围内。为了满足这类投保人的需求,本文引入了投保人的净损失约束,研究在该约束下投保人的最优保险问题。研究表明:如果Arrow模型的解满足该约束,本模型的解与Arrow模型解一致,最优保单是有且仅有一个免赔额的部分保险契约,否则最优保单将存在两个免赔额。投保人效用最优时,本模型在应对高损时所提供的赔付水平始终不低于Arrow模型,而本模型在应对低损时对于IARA(DARA/CARA)型投保人所提供的赔付水平依次要低于(高于/等于)Arrow模型。此外,投保人的期望效用将随着其净损失上限的提高而逐渐增大,直到Arrow模型的解满足该约束时其效用达到最大。

隐变量模型及其在贝叶斯运营模态分析的应用

贝叶斯FFT算法是运营模态分析的最新一代算法,以其准确性高、计算速度快、可有效进行不确定性度量等优点受到广泛关注。然而,现有贝叶斯FFT算法针对不同情况(稀疏模态、密集模态、多步测试等)需采用不同优化算法,且编程实现极为复杂。为此,本文旨在提出针对不同情况的贝叶斯FFT算法的统一框架,并实现模态参数的高效求解;视结构模态响应为隐变量,建立贝叶斯模态识别单步测试和多步测试的隐变量模型框架;针对提出的隐变量模型运用期望最大化算法实现各种情况下模态参数的统一贝叶斯推断,利用隐变量解耦模态参数优化过程,采用Louis等式间接求取似然函数的Hessian矩阵。通过两个实际工程测试案例,并与现有方法对比,验证所提方法的准确性和高效性。分析结果表明,本文所提算法与现有方法结果相同,但其推导简单、易编程,尤其对于密集模态识别问题具有明显的计算优势。本文为贝叶斯模态识别建立起统一的隐变量模型框架,在很大程度上简化原本繁琐且冗长的推导过程,提高计算效率,同时也为应用变分贝叶斯、吉布斯采样等算法求解贝叶斯模态识别问题提供了可能。

考虑车流速度分布扰动的干道绿波最大带宽协调控制

城市车辆速度受多种因素影响呈现某种分布,鲁棒的干道协调控制模型应考虑不同速度下的车辆带宽需求。针对速度波动变化干道传统协调控制方法适用性差的问题,分析了带干扰的干道车辆速度时空分布特征,以推荐速度带宽最大与期望带宽最大为目标,提出了考虑车流速度分布扰动的干道绿波最大带宽协调控制方法。针对佛山市同济路4交叉口干道,速度分布扰动干道协调方法能够提供更符合车辆分布特性的协调方案,比Maxband模型和改进的Maxband模型提升约70%和14%的期望带宽。研究结果表明:笔者方法能够降低Maxband模型平均停车次数25%,平均延误45%和平均排队长度17%;针对不同速度期望和方差情况下的灵敏度分析实验表明,该方法能够为干道提供更具有鲁棒性的干道协调控制方案。

Modeling Cyber Loss Severity Using a Spliced Regression Distribution with Mixture Components

Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.

认知视角下社交媒体持续使用行为的元分析

针对社交媒体持续性使用行为的实证研究结果存在不一致现象,有必要从平台认知层面和个体认知层面进行统一,明确社交媒体用户持续使用行为的前置变量与后置变量的关系并验证关键调节变量。文章构建认知视角下社交媒体持续使用行为的理论模型,筛选出包含15227个样本的41篇中英文实证类文献。采用元分析方法,基于期望确认模型综合分析12组变量间的关系强度,从性别、社会群体以及平台类型三个维度,探讨影响因素与社交媒体持续使用行为间的调节效应。结果表明:社交媒体用户持续使用模型中12组变量均显著相关,验证了期望确认模型在社交媒体领域的普适性,平台和个体认知因素对用户满意度以及持续使用意愿均有正向影响,调节变量性别与平台类型在变量之间具有部分调节作用,其中工作学习型平台的调节作用最强。

期望扇区三矢量模型预测电流控制

三矢量模型预测电流控制需要依次计算6扇区电压矢量的作用时间,再由价值函数选取期望电压矢量,存在计算量大的缺点。本文提出一种基于期望扇区的三矢量模型预测电流控制,以快速确定期望电压矢量。首先,建立d-q轴的三矢量电流模型,给出其三矢量作用时间;然后,令预测电流为期望电流,可以获得期望电压矢量,并利用Clark变换得到α-β轴的期望电压矢量;最后,利用反正切函数求得期望电压矢量角度,确定期望电压矢量所在的期望扇区,得到期望三矢量及其作用时间。仿真和实验表明,该方法能够保持很好的控制系统性能。同时,该方法只需一次计算期望电压矢量角度即可确定期望三矢量及其作用时间,比传统三矢量方法有效缩短了算法执行时间约60%。