### 统计代写|决策与风险作业代写decision and risk代考|An Integrated Bayesian BWM

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|Classifiable TOPSIS Model

Risk assessment has been regarded as one of the scientific fields for about 40 years. Initially, how to conceptualize risk assessment was a question discussed by researchers at that time. After that, many concepts, principles, theories, methods,

and frameworks for assessing risk have been gradually developed. These studies still deeply influence the field of risk assessment (Aven 2016). Many novel risk assessment methods have brought many contributions and new trends in this field. Newer, more effective, more comprehensive, and more systematic assessment models are the common goal of researchers. A risk decision model is proposed by Hansson and Aven (2014), the model divides risk assessment into five stages, including evidence, knowledge base, broad risk evaluation, decision-makers review, and decision. The first three stages are fact-based, providing evidence through testing and collecting data or information about risk events. Related expert groups are based on these data or information to further research and analyze risks. In addition, the last three stages are value-based. Because risk events are complex and difficult to fully illustrate and explain through scientific tools, after extensive risk assessment, it is necessary to review and judge by decision-makers before final decisions can be made. The purpose of risk analysis is to eliminate and control potential risk events or hazardous factors, so as to reduce the occurrence of accidents and diminish the severity of accidents (Lo et al. 2019).

Research on risk analysis can be divided into three types, including qualitative analysis, semi-quantitative analysis, and quantitative analysis (Marhavilas et al. 2011 ; Mutlu and Altuntas 2019). Qualitative analysis is to explore whether the research subjects have special attributes or characteristics, and whether they are related or not, through observation and analysis experiments. The data recorded through expert interviews is a way of qualitative analysis. Common qualitative analysis techniques are checklist, what if analysis, safety audits, task analysis, sequentially timed events plotting (STEP), human factors analysis and classification system (HFACS), and hazard and operability study (HAZOP). Quantitative analysis is the quantitative relationship among the components contained in a research object, or the quantitative relationship among the characteristics that it possesses, and it can also analyze and compare the special relationships, characteristics, and properties of several objects quantitatively simultaneously. Therefore, the analysis results are mostly described and solved in terms of quantity. Quantitative analysis techniques include clinical risk and error analysis (CREA), proportional risk-assessment (PRAT), decision matrix risk-assessment (DMRA), societal risk, predictive epistemic approach (PEA), and quantitative risk-assessment (QRA). The semi-quantitative analysis is somewhere in between. In the risk assessment, the collected observation data and survey information use semi-quantitative analysis to perform risk assessment. Semi-qualitative quantitative analysis techniques include event tree analysis (ETA), failure mode and effects analysis (FMEA), fault tree analysis (FTA), risk-based maintenance (RBM), and human error analysis techniques (HEAT/HFEA). The above-mentioned risk assessment and analysis tools are popular methods used by academic researchers and risk analysts today (Marhavilas et al. 2011; Lo et al. 2019; Mutlu and Altuntas 2019; Chang et al. 2019; Gul et al. 2020; Yucesan and Gul 2020; Lo et al. 2020; Liou et al. 2020).

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

The FMEA program commences with a review of design details, illustrations of equipment block diagrams, and recognition of all possible failures, consecutively. Following recognition, all potential causes and effects should be classified to the related failure modes. After this, failure modes are prioritized based on their destructive effects and ranked by a risk rating (Lo and Liou 2018). More methods are being combined with FMEA to optimize the risk assessment model, with MCDM being the most outstanding (Huang et al. 2020). The relevant MCDM-based FMEA model in the past five years is presented in Table 2.1.

The above-mentioned literatures have made great contributions to the field of risk assessment, making semi-quantitative analysis more effective. After extensive FMEA literature review, this study found some research gaps. As mentioned earlier, FMEA is based on risk analyst judgement to construct a risk assessment matrix. However, in some cases, if risk analysts cannot make appropriate semantic judgments, they can only compare the risk levels from the existing failure modes. In addition, only three risk factors are considered in most RPN calculations, and only a few researches have discussed the management costs of failure modes.

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

The operation of BWM is easy to understand and to obtain highly consistent results, so it has been widely used in decision-making problems in various industries. BWM plays a very important role in this research. It is not only used to evaluate the weights of risk factors, but also used to evaluate the degrees of risks of failure modes. BWM was proposed by Rezaei (2015), and it is mainly used to overcome the limitations and shortcomings of AHP. Figure $2.1$ shows a schematic diagram of the conventional pairwise comparison method. The evaluation system has five evaluated items, and the number of pairwise comparisons is $10[n(n-1) / 2 \Rightarrow 5 *(4) / 2=10]$. The pairwise comparison concept proposed by BWM is shown in Fig. 2.2. Experts or decisionmakers or risk analysts select the most and least important evaluated items (best and worst evaluated items), and then make pairwise comparisons. Using the same example, BWM only needs 7 pairwise comparisons $[2 n-3 \Rightarrow 2 *(5)-3=7]$.

Tables $2.2,2.3$, and $2.4$ illustrate the questionnaire design pattern associated with the AHP and BWM. From the perspective of the experts or decision-makers or risk analysts answering the questionnaires, the BWM questionnaire is more logical and consistent. In Tables $2.2,2.3$, and $2.4$, the light gray shaded cells indicate the evaluation information input by the experts. In the AHP example, considering the goal (evaluation issue), a number from $1 / 9$ to 9 is assigned to show the preference of a specific evaluated item over the others (filled in light gray shaded cells). It is worth mentioning that BWM uses a number between 1 and 9 to show the preference of evaluated item $i$ over the evaluated item $j$.
The steps of BWM in FMEA can be summarized as follows (Rezaei 2015):
Step 1. Construct a set of risk factors.
The experts or decision-makers or risk analysts form an FMEA team, and they formulate evaluation risk factors $\left(C_{1}, C_{2}, \ldots, C_{j}, \ldots, C_{n}\right)$ for decision-making problems.

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

FMEA 计划从连续审查设计细节、设备框图图解和识别所有可能的故障开始。识别后，应将所有潜在的原因和影响归类为相关的故障模式。在此之后，故障模式根据其破坏性影响进行优先级排序，并按风险等级排序（Lo and Liou 2018）。更多方法正在与 FMEA 相结合以优化风险评估模型，其中 MCDM 最为突出（Huang et al. 2020）。过去五年中基于 MCDM 的相关 FMEA 模型如表 2.1 所示。

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

BWM的操作简单易懂，结果一致性高，因此被广泛应用于各行业的决策问题。BWM 在这项研究中起着非常重要的作用。它不仅用于评估风险因素的权重，还用于评估故障模式的风险程度。BWM 由 Rezaei (2015) 提出，主要用于克服层次分析法的局限性和不足。数字2.1显示了传统的成对比较方法的示意图。评价系统有五个评价项目，两两比较的次数为10[n(n−1)/2⇒5∗(4)/2=10]. BWM 提出的成对比较概念如图 2.2 所示。专家或决策者或风险分析师选择最重要和最不重要的评估项目（最佳和最差评估项目），然后进行成对比较。使用同样的例子，BWM 只需要 7 次成对比较[2n−3⇒2∗(5)−3=7].

FMEA 中 BWM 的步骤可总结如下（Rezaei 2015）：

## 广义线性模型代考

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