### 统计代写|决策与风险作业代写decision and risk代考|Simulation Output Analysis for Risk

statistics-lab™ 为您的留学生涯保驾护航 在代写决策与风险decision and risk方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写决策与风险decision and risk方面经验极为丰富，各种代写决策与风险decision and risk相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|决策与风险作业代写decision and risk代考|Assessment and Mitigation

According to Aven (2016), the area of risk assessment and management has evolved considerably since its beginnings in the $1970 \mathrm{~s}$, and there have been developed a wide variety of methods and applications in most societal sectors. As evidence of this evolution, we can observe the variety of research groups of the Society for Risk Analysis, among which we can mention: Dose Response, Ecological Risk Assessment, Emerging Nanoscale Materials, Engineering and Infrastructure, Exposure Assessment, Microbial Risk Analysis, Occupational Health and Safety, Risk Policy and Law, and Security and Defense.

Aven (2016) also mentions that the area of risk assessment and management has two fundamental tasks: (i) to use risk assessments and management to study and treat the risk caused by the execution of specific activities (for example, the operation of an offshore facility or investment), and (ii) conduct research and development (in

general) on risk, developing concepts, theories, frameworks, approaches, principles, methods and models to understand, evaluate, characterize, communicate and (in a broad sense) manage and mitigate risk.

Parallel to the development of the area of risk assessment and mitigation, concepts, techniques and available tools (software) have been developed for systems simulation and, in particular, for stochastic simulation, which is the type of simulation that allows us to include uncertainty and risk components in a model. In practice, a model for risk management can become complex, in the sense that we cannot obtain analytical expressions for the risk measures that are relevant to the problem under study and, in such circumstances, stochastic simulation has particular relevance for the estimation (from the output of simulation experiments) of the risk measures to be mitigated.
The objective of this Chapter is to present a review of the techniques that have been proposed to analyze the output of simulation experiments, in order to estimate performance measures that are important to conduct a risk assessment and mitigation study, when a simulation model is used to imitate the evolution of a system.

The Chapter is organized as follows. After this introduction, we present a brief literature review on the relevant applications of systems simulation for risk assessment and mitigation. In the next section, we present an overview of the necessary concepts and tools available to conduct simulation experiments. The following section discusses the most important techniques for estimating risk measures in transient simulations, including the estimation of expectations, variances and risk measures based on quantiles and M estimators. In this Section, we also present a Bayesian framework to incorporate parameter uncertainty in the process of estimating risk measures. Finally, the last section discusses the techniques available to estimate risk measures in steady-state simulations, considering again the estimation of expectations, variances, and quantile-based risk measures. In the last section we also discuss the initial transient problem and how can it be mitigated.

## 统计代写|决策与风险作业代写decision and risk代考|Literature Review

In this section we present a brief review on the main literature related to simulation applications that have been successfully applied for risk assessment and mitigation in different areas. The literature on the applications of risk assessment and mitigation is abundant, and this is only a very brief review on the applications of simulation in this area, for a more detailed review on the applications of risk assessment and mitigation, the reader is referred to Aven (2016).

According to Aven (2016), an important step in the process of making informed decisions for risk management corresponds to risk assessment, which consists of the analysis of the knowledge base to have an understanding about the risks and the uncertainties related to the case under study. As explained in Aven (2012), although it is true that the criteria for evaluating risks are usually based on the estimation of expected values (e.g., the cost of a negative event) or probabilities (of a negative event), we can find arguments for the use of other measures for risk assessment.

For example, in the area of finance, risk measures have been proposed based not only on the estimation of expected losses, but also on quantile-based measures, such as the Value at Risk (VaR) or the Conditional Value at Risk (CVar), see e.g., Natarajan et al. (2009). Because of these reasons, in addition to the techniques for estimating expectations and probabilities from the output of simulation experiments, in this Chapter we will also deal with the estimation of other risk measures, such as the variance and risk measures based on quantiles and $\mathrm{M}$ estimators, recognizing that some other measures for risk management and mitigation could be proposed in addition to the ones discussed in this Chapter.

Stochastic simulation has been widely used for risk assessment in various areas, for example, in supply chain management, where risk measures are mainly related to shortages, the occurrence of catastrophes and the costs incurred (see, e.g., Wu and Olson 2008; Wu et al. 2012; Chen et al. 2013; Hamdi et al. 2018; Oliveira et al. 2019 ; and their references). Stochastic simulation has also been used extensively in the areas related to production planning to design products with high reliability, for example, for water distribution (see, e.g., Wagner et al. 1988; Ostfeld et al. 2002), for the design of integrated circuits (see, e.g., Hu 1992; Wang et al. 2007; Li et al. 2008), or for the design of highly reliable products (see, e.g., Heidelberger 1995; Juneja and Shahabuddin 2006; Bucklew 2013). One area of production planning where stochastic simulation is particularly important for risk mitigation is operations scheduling, where the achievement of programs that meet delivery dates is very important (see, e.g., Pegden 2017; Smith et al. 2019).

In areas related to health care, stochastic simulation experiments have also been successfully conducted, for example, to design spaces for medical care with a low risk of experiencing long waiting times (see, e.g., Fone et al. 2003), to improve the understanding and mitigation of epidemics (see, e.g., Salathe et al. 2012), to make economic evaluations of diseases and their treatments (see, e.g., Cooper et al. 2006). A more complete review of the applications of simulation for health care can be found in Mielczarek and Uziałko-Mydlikowska (2012).

Simulation has been successfully applied in the areas of waste treatment and energy recovery (see, e.g., Ren et al. 2010; Ren 2018; Liang et al. 2020; Yang et al. 2020), and to mitigate the risk of the occurrence of landslides (see, e.g., Dai et al. 2002; Fell et al. 2005), or to quantify the resilience of power systems (see, e.g., Pantelli et al. 2017) or urban infrastructure (see e.g., Ouyang and Duenas-Osorio 2012).

## 统计代写|决策与风险作业代写decision and risk代考|Systems Simulation

The term system is used in various disciplines to identify the elements and dynamics of a phenomenon that is intended to be understood, analyzed and/or designed from the point of view of the corresponding discipline. According to Schmidt and Taylor (1970), a system is a collection of entities that interact to achieve a goal. For example, in Industrial Engineering we study industrial systems (supply chains, service centers, manufacturing plants, etc.) that consist of raw materials, human resources and capital, organized to efficiently produce and distribute manufactures and/or services. In the same way, systems can be studied in Economics from the point of view of the welfare of the agents involved in the economic phenomenon and, similarly, each discipline study systems from its analytical perspective.

Without a doubt, humanity has studied systems from very ancient times. Initially, an attempt was made to understand natural systems through experimentation with the real system. The search for knowledge led to the development, first of physical models of systems (prototypes, scale models, etc.) that allowed them to carry out controlled experiments, and later, theories and mathematical models that could explain and predict the behavior of systems, both existing ones and those that were developed. A physical model is an imitation, generally simpler, of a real system, whose experimentation (under controlled conditions) allows us to study the behavior of the system in a natural way, as it would happen with the real system. A mathematical model, on the other hand, represents the system to be studied by means of mathematical relationships; therefore, by experimenting with it, we can predict the behavior of the relevant variables of the system and imagine the main behavior of the system, even if it is not physically reproduced.

One of the purposes of a mathematical model is to predict the behavior of one or more characteristics of the system (known as response variables) based on other variables (called control variables). A mathematical model in which, through a set of equations, the response variables are expressed as a (explicit) function of the control variables is very convenient to predict the behavior of a system, and we say that the model has an analytical solution when this set of equations exists.

However, when we want to study a system in great detail, we must consider variables whose relationships are not easy to solve to find an analytical solution. Nonetheless, the model can still be useful to analyze the system, since for this purpose numerical methods have been developed. Given particular values for the control variables, numerical methods allow us to calculate, by using a computer, the value of the response variables.

Among the numerical methods used to study a system (see Fig. 6.1), simulation has the fundamental characteristic that the model tries to imitate the behavior of the system under study, in order to calculate, with the help of a computer, the value of the system’s response variables. For the purposes of this Chapter, we will recognize by simulation the computer imitation of the behavior of a system, using a (mathematical) model to explain its relevant characteristics, in order to numerically evaluate the performance measures of the system.

## 统计代写|决策与风险作业代写decision and risk代考|Assessment and Mitigation

Aven（2016）还提到风险评估和管理领域有两个基本任务：（i）使用风险评估和管理来研究和处理由执行特定活动（例如，离岸设施或投资），以及（ii）进行研究和开发（在

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。