### 统计代写|非参数统计代写Nonparametric Statistics代考|STATS205

statistics-lab™ 为您的留学生涯保驾护航 在代写非参数统计Nonparametric Statistics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。我们的专家在代写非参数统计Nonparametric Statistics代写方面经验极为丰富，各种代写非参数统计Nonparametric Statistics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|非参数统计代写Nonparametric Statistics代考|Efficiency of Nonparametric Methods

Doubt is not a pleasant condition, but certainty is absurd.
Francois Marie Voltaire (1694-1778)
It would be a mistake to think that nonparametric procedures are simpler than their parametric counterparts. On the contrary, a primary criticism of using parametric methods in statistical analysis is that they oversimplify the population or process we are observing. Indeed, parametric families are not more useful because they are perfectly appropriate, rather because they are perfectly convenient.

Nonparametric methods are inherently less powerful than parametric methods. This must be true because the parametric methods are assuming more information to construct inferences about the data. In these cases the estimators are inefficient, where the efficiencies of two estimators are assessed by comparing their variances for the same sample size. This inefficiency of one method relative to another is measured in power in hypothesis testing, for example.

However, even when the parametric assumptions hold perfectly true, we will see that nonparametric methods are only slightly less powerful than the more presumptuous statistical methods. Furthermore, if the parametric assumptions about the data fail to hold, only the nonparametric method is valid. A $t$-test between the means of two normal populations can be dangerously misleading if the underlying data are not actually normally distributed. Some examples of the relative efficiency of nonparametric tests are listed in Table 1.1, where asymptotic relative efficiency (ARE) is used to compare parametric procedures (second column) with their nonparametric counterparts (third column). ARE describes the relative efficiency of two estimators of a parameter as the sample size approaches infinity and is listed for the normal distribution, where parametric assumptions are justified, and the double-exponential distribution. For example, if the underlying data are normally distributed, the $t$-test requires 955 observations to have the same power of the Wilcoxon signed-rank test based on 1000 observations.

Parametric assumptions allow us to extrapolate away from the data. For example, it is hardly uncommon for an experimenter to make inferences about a population’s extreme upper percentile (say, 99th percentile) with a sample so small that none of the observations would be expected to exceed that percentile. If the assumptions are not justified, this is grossly unscientific.

## 统计代写|非参数统计代写Nonparametric Statistics代考|Overconfidence Bias

Confirmation Bias or Overconfidence Bias describes our tendency to search for or interpret information in a way that confirms our preconceptions. Business and finance has shown interest in this psychological phenomenon (Tversky and Kahneman, 1974) because it has proven to have a significant effect on personal and corporate financial decisions where the decision maker will actively seek out and give extra weight to evidence that confirms a hypothesis they already favor. At the same time, the decision maker tends to ignore evidence that contradicts or disconfirms their hypothesis.

Overconfidence bias has a natural tendency to affect an experimenter’s data analysis for the same reasons. While the dictates of the experiment and the data sampling should reduce the possibility of this problem, one of the clear pathways open to such hias is the infusion of parametric assumptions into the data analysis. After all, if the assumptions seem plausible, the researcher has much to gain from the extra certainty that comes from the assumptions in terms of narrower confidence intervals and more powerful statistical tests.

Nonparametric procedures serve as a buffer against this human tendency of looking for the evidence that best supports the researcher’s underlying hypothesis. Given the subjective interests behind many corporate research findings, nonparametric methods can help alleviate doubt to their validity in cases when these procedures give statistical significance to the corporation’s claims.

# 非参数统计代考

## 统计代写|非参数统计代写Nonparametric Statistics代考|Efficiency of Nonparametric Methods

Francois Marie Voltaire (1694-1778)

## 有限元方法代写

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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。