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

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|Consensus Reaching

In this phase, fuzzy risk assessment involving the presence of uncertainty in the heterogeneous preferences elicited by the risk analysts is defined in the form of $Z$ grey numbers. As both restriction of the preferences elicited by the risk analysts and the reliability of the restriction are grey numbers, the fuzzy risk assessment representations can exist in the form of white numbers (for completely known risk analysts’ preferences elicitation), black numbers (completely unknown risk analysts’ preferences elicitation) and grey numbers (partially known/unknown risk analysts’ preferences elicitation). Since, the grey numbers forms are distinct from one to another (as in Table 4.1), a novel fuzzy agreement relation approach is developed for the first time to define fuzzy risk assessment representations involving the presence of uncertainty for each heterogeneous form of preferences elicited by the risk analysts as a

single common form. The approach involves the transformation of Z-grey number into Z-number, where the transformation is given as follows.

Let $P_{S, t}^{\prime}$ and $Q_{S, t}^{\prime}$ be the probability of failure and the severity of loss, respectively, in the form of Z-grey numbers define as $P_{S_{i, k}}^{\prime}=\left[H_{P_{s_{i, k}}^{G}}^{G}, L_{P_{s, t, k}^{\prime}}^{G}\right]$ and $Q_{S_{i, k}}^{\prime}=\left[H_{Q_{s_{i, k}}^{G}}^{G}, L_{Q_{s_{i, k}}^{G}}^{G}\right]$, where $H_{P_{s_{s, k}}^{G}}^{G}$ and $H_{Q_{s_{i, k}}^{G}}^{G}$ are the restriction of the preferences elicited by the risk analysts for $P_{S_{i, k}^{\prime}}^{\prime}$ and $Q_{S_{i, t}}^{\prime}$ respectively, while $L_{P_{S_{,}, k}^{\prime}}^{G}$ and $L_{Q_{s, t, k}^{\prime}}^{G}$ are the reliability of the restriction for $P_{S_{i, k}^{\prime}}^{\prime}$ and $Q_{S_{i, k}}^{\prime}$, respectively.

1. If $P_{S, k}^{\prime} \in[0,1]$ and $Q_{S_{i, k}}^{\prime} \in[0,1]$ are $Z$-grey numbers that represent the preferences elicited by the risk analysts that are completely known, then $P_{S_{i, k}}^{\prime}$ and $Q_{S_{i, k}}^{\prime}$ are transformed into Z-numbers, $P_{S_{i, k}}^{}$ and $Q_{S_{i, k}}^{}$, respectively using the transformation function, $T_{\sigma}, \sigma=P_{S_{i, k},}^{\prime}, Q_{S_{i, i}}^{\prime}$, given as the following Eqs. (4.2) and (4.3).
$$T_{P_{c_{i, k}}^{\prime}}:[0,1] \rightarrow P_{S_{i, k}^{}}^{}=\left[H_{P_{s_{, k}}^{}}^{G}, L_{P_{s_{i, k}}^{}}^{G}\right]$$
and
$$T_{Q_{c_{i k}}^{\prime}}:[0,1] \rightarrow Q_{S_{i, k}}^{*}=\left[H_{Q_{s_{i, k}}^{G}}^{G}, L_{Q_{s_{i, k}}^{G}}^{G}\right]$$
2. If $P_{S_{i, k}}^{\prime} \in[0,1]$ and $Q_{S_{i, k}}^{\prime} \in[0,1]$ are Z-grey numbers that represent the preferences elicited by the risk analysts that are completely unknown, then $P_{S_{i, k}}^{\prime}$ and $Q_{S_{i, k}}^{\prime}$ are transformed into Z-numbers, $P_{S_{j, k}}$ and $Q_{S_{i, i}}^{}$, respectively using the transformation function, $T_{v}, v=P_{S_{i, k}}^{\prime}, Q_{S_{i, k}}^{\prime}$, given as the following Eqs. (4.4) and (4.5). $$T_{P_{c_{i, k}^{\prime}}}:[0,1] \rightarrow P_{S_{i, k}^{}}^{}=\left[H_{P_{s_{i, k}}^{G}}^{G}, L_{P_{s_{i, k}}^{g}}^{G}\right]$$ and $$T_{Q_{C_{i, t}}^{\prime}}:[0,1] \rightarrow Q_{S_{i, k}}^{}=\left[H_{Q_{s_{i, k}}^{\xi}}^{G}, L_{Q_{s_{i, k}}^{G}}^{G}\right]$$

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

Note that from Phase 1, the current form for the fuzzy risk assessment representations involving the presence of uncertainty in the heterogeneous preferences elicited by the risk analysts is a single consensus form, which is the Z-numbers. The representation of the obtained single consensus form, however, is too complex in nature (Bakar and Gegov 2015; Kang et al. 2012; Zadeh 2011). Thus in this phase, this study converts the obtained single consensus form into a much simpler consensus form, which is the Z-fuzzy number. The conversion which involves incorporation of defuzzified value of the risk reliability into the risk restriction component, converts the obtained single consensus form (Z-numbers) $P_{S_{i, k}^{}}^{}$ and $Q_{S_{i, t}}^{*}$ into the reduced consensus form (Z-fuzzy numbers), $P_{S_{i, k}}^{o}$ and $Q_{S_{i, k}}^{o}$, respectively (Bakar and Gegov 2015; Kang et al. 2012). Details on the conversion are given by the following procedures (Bakar and Gegov 2015; Kang et al. 2012).

Step 1: Obtain the defuzzified value, $T_{n}$, of $L_{P_{S_{i}^{}, k}^{G}}^{G}$ and $L_{Q_{\dot{\xi}, k}^{}}^{G}$ for both $P_{S_{i, k}^{}}^{}$ and $Q_{S_{i, k}^{}}^{}$, respectively, using the following Eq. (4.8).
$$T_{n}=\frac{1}{3}\left[b_{n 1}+b_{n 2}+b_{n 3}+b_{n 4}-\frac{b_{n 3} b_{n 4}-b_{n 1} b_{n 2}}{\left(b_{n 3}+b_{n 4}\right)-\left(b_{n 1}+b_{n 2}\right)}\right]$$
where $n=L_{P_{s_{i, k}^{}}^{G}}^{G}, L_{Q_{s_{i, k}^{}}^{G}}^{G}$.
Step 2: Incorporate $T_{n}$ into $H_{P_{S_{i, k}^{}}^{G}}^{G}$ and $H_{Q_{s_{i, k}}^{}}^{G}$ for both $P_{S_{i, k}^{}}^{}$ and $Q_{S_{i, k}^{}}^{}$, respectively, using the following Eq. (4.9).
$$X_{m}=\left[T_{n} * a_{m 1}, T_{n} * a_{m 2}, T_{n} * a_{m 3}, T_{n} * a_{m 4} ; 1\right]=\left[\bar{a}{m 1}, \bar{a}{m 2}, \bar{a}{m 3}, \bar{a}{m 4} ; 1\right]$$
where $X=P_{S_{i, k}}^{o}, Q_{S_{i, k}}^{o}$ and $m=H_{P_{S_{i, k}^{}}^{G}}^{G}, H_{Q_{s_{i, k}}^{}}^{G}$.

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

In phase 2 , fuzzy risk assessment representations involving the presence of uncertainty in the heterogeneous preferences elicited by the risk analysts in the form of

Z-grey number, has successfully converted into the reduced consensus forms (Zfuzzy numbers). This reduced consensus forms are then aggregated using a novel fuzzy risk evaluation rating method to assess the correct level of risks, such that the assessments are consistent with the presence of uncertainty in the heterogeneous preferences elicited by the risk analysts. Steps provided in this phase are similar to established methods (Bakar and Gegov 2014, 2015; Baker et al. 2019, 2020), only that the proposed novel method uses Z-grey numbers. Details on the proposed novel fuzzy risk evaluation rating method are given as the following.

Step I: Evaluate the interaction score, $S_{i}$, between $P_{S_{i, t}}^{o}$ and $Q_{S_{i, t}}^{o}$ for each risk under consideration as
$$S_{i}=\frac{\sum_{i, k=1}^{n}\left(P_{S_{i, k}}^{o} \times Q_{S_{i, k}}^{o}\right)}{\sum_{i, k=1}^{n}\left(Q_{S_{i, k}}^{o}\right)}$$
Step 2: Compute the centroid- $x$ component value for $S_{i}$ as
$$x_{S_{i}}=\frac{1}{3}\left[a_{1 S_{i}}+a_{2 S_{i}}+a_{3 S_{i}}+a_{4 S_{i}}-\frac{a_{3 S_{i}} a_{4 S_{i}}-a_{1 S_{i}} a_{2 S_{i}}}{\left(a_{3 S_{i}}+a_{4 S_{i}}\right)-\left(a_{1 S_{i}}+a_{2 S_{i}}\right)}\right]$$
and the centroid- $y$ component value for $S_{i}$ as
$$y_{S_{i}}=\frac{w_{S_{i}}}{3}\left[1+\frac{a_{3 S_{i}} a_{4 S_{i}}-a_{1 S_{i}} a_{2 S_{i}}}{\left(a_{3 S_{i}}+a_{4 S_{i}}\right)-\left(a_{1 S_{i}}+a_{2 S_{i}}\right)}\right]$$
where $x_{S_{i}} \in[0,1]$ and $y_{S_{i}} \in[0,1]$.
Step 3: Obtain the deviation of centroid component value for $S_{i}$ as
$$\psi_{S_{i}}=\left|a_{4 S_{i}}-a_{1 S_{i}}\right| \times y_{S_{i}}$$

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

1. 如果磷小号,到′∈[0,1]和问小号一世,到′∈[0,1]是从- 灰色数字，代表完全已知的风险分析师引发的偏好，然后磷小号一世,到′和问小号一世,到′转换为 Z 数，磷小号一世,到和问小号一世,到，分别使用变换函数，吨σ,σ=磷小号一世,到,′,问小号一世,一世′，给出如下等式。(4.2) 和 (4.3)。
吨磷C一世,到′:[0,1]→磷小号一世,到=[H磷s,到G,大号磷s一世,到G]

吨问C一世到′:[0,1]→问小号一世,到∗=[H问s一世,到GG,大号问s一世,到GG]
2. 如果磷小号一世,到′∈[0,1]和问小号一世,到′∈[0,1]是 Z 灰色数字，代表完全未知的风险分析师引发的偏好，然后磷小号一世,到′和问小号一世,到′转换为 Z 数，磷小号j,到和问小号一世,一世，分别使用变换函数，吨v,v=磷小号一世,到′,问小号一世,到′，给出如下等式。(4.4) 和 (4.5)。吨磷C一世,到′:[0,1]→磷小号一世,到=[H磷s一世,到GG,大号磷s一世,到GG]和吨问C一世,吨′:[0,1]→问小号一世,到=[H问s一世,到XG,大号问s一世,到GG]

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

Step 1：获取去模糊化后的值，吨n， 的大号磷小号一世,到GG和大号问X˙,到G对彼此而言磷小号一世,到和问小号一世,到，分别使用以下等式。(4.8)。

X米=[吨n∗一种米1,吨n∗一种米2,吨n∗一种米3,吨n∗一种米4;1]=[一种¯米1,一种¯米2,一种¯米3,一种¯米4;1]

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

Z-灰色数，已成功转换为简化共识形式（Zfuzzy numbers）。然后使用一种新的模糊风险评估评级方法汇总这种简化的共识表格，以评估正确的风险水平，以便评估与风险分析师引发的异质偏好中存在的不确定性一致。此阶段提供的步骤类似于已建立的方法（Bakar and Gegov 2014, 2015; Baker et al. 2019, 2020），只是所提出的新方法使用 Z-grey 数。所提出的新型模糊风险评估评级方法的详细信息如下。

X小号一世=13[一种1小号一世+一种2小号一世+一种3小号一世+一种4小号一世−一种3小号一世一种4小号一世−一种1小号一世一种2小号一世(一种3小号一世+一种4小号一世)−(一种1小号一世+一种2小号一世)]

ψ小号一世=|一种4小号一世−一种1小号一世|×是小号一世

## 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。