### 统计代写|r语言作业代写代做|Kurtosis

R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

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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|r语言作业代写代考|Kurtosis

Kurtosis is a measure of the degree to which portfolio returns appear in the tails of their distribution. A normal distribution has a kurtosis of 3 , which follows from the fact that a normal distribution does have some of its mass in its tails. A distribution with a kurtosis greater than 3 has more returns in its tails than the normal, and one with kurtosis less than 3 has fewer returns in its tails than the normal. That matters to investors because more bad returns in the tails means that our portfolio might be at risk of a rare but huge downside event. The terminology is a bit confusing because negative kurtosis actually is less risky because it has fewer returns in the tails.

Kurtosis is often described as negative excess or positive excess, and that is in comparison to a kurtosis of 3. A distribution with negative excess kurtosis equal to $-1$ has an absolute kurtosis of 2 , but we subtract 3 from 2 to get to $-1$. Remember, though, the negative kurtosis means fewer returns in the tails and, probably, less risk.

Here is the equation for excess kurtosis. Note that we subtract 3 at the end:
$$\text { Kurtosis }=\sum_{t=1}^{n}\left(x_{i}-\bar{x}\right)^{4} / n /\left(\sum_{t=1}^{n}\left(x_{i}-\bar{x}\right)^{2} / n\right)^{2}-3$$
The code flows for calculating kurtosis and rolling kurtosis are quite similar to those for skewness, except we use the built-in kurtosis () function. That was by design, we want to write code that can easily be reused for another project.

## 统计代写|r语言作业代写代考|Rolling Kurtosis in the xts world

Calculating rolling kurtosis in the xts world uses the same code flow as we used for skewness, except we replace $F U N=$ skewness with FUN $=$ kurtosis.In the tidyverse plus tibbletime paradigm, we return to the rollify() code flow.In the tidyquant world, we, again, wrap rollapply with tq_mutate() and then call FUN $=$ kurtosis.

FIGURE 6.5: Rolling Kurtosis ggplot
Figure $6.5$ looks how we were expecting since it’s the same data as we used for Figure 6.4.

That’s all for our work on kurtosis, which was made a lot more efficient by our work on skewness. Now let’s create one app for interactively visualizing both skewness and kurtosis.To wrap our skewness and kurtosis work into a Shiny application, we start with the same sidebar for stocks, weights, starting date and rolling window, as shown in Figure 6.6.

FIGURE 6.6: www.reproduciblefinance.com/shiny/skewness-kurtosis
Let’s get into the code for this app.
There are three crucial eventReactive() chunks.
First, we calculate portfolio returns based on user input and stay in the xts world.

## 统计代写|r语言作业代写代考|Concluding Risk

That completes our section on risk, which we treated as the variability of portfolio returns. We have explored several code flows for measuring historical and rolling standard deviation, skewness and kurtosis. From a data science toolkit perspective, we have delved into descriptive statistics for our portfolio, focusing on the variability or dispersion of returns.

One important take-away from this section is how we reused our own code flows to accelerate future work. Our work on skewness was facilitated by our work on standard deviation, and our work on kurtosis flowed from the work on skewness. Writing clear, reproducible code in the first chapter might have taken us a bit more time up front, but it had a nice efficiency payoff in the future chapters. When our work becomes more complex in the real world, those future efficiencies become ever more important. Hopefully this chapter has convinced us that writing good code is not simply an aesthetic nice-to-have it has tangible benefits by saving us time.

If you are starting a new $\mathrm{R}$ session and wish to run our code for the different risk measures calculated in this section, first get the data objects.

## 统计代写|r语言作业代写代考|Kurtosis

峰度 =∑吨=1n(X一世−X¯)4/n/(∑吨=1n(X一世−X¯)2/n)2−3

## 广义线性模型代考

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## 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。