kurtosis

简明释义

英[kəˈtəʊsɪs]美[kɜːrˈtosɪs]

n. 峰态，峰度，峭度

英英释义

单词用法

同义词

反义词

例句

1.The term -3 is added in order to ensure that the normal distribution has zero kurtosis.

其中的- 3是为了确保正态分布的峰度为零。

2.In the security market, return-loss distribution exist the severe phenomenon of excess kurtosis and heavy tail;

证券市场上收益率分布存在严重的偏峰厚尾现象；

3.Jointed the constant module (CM) character of digital modulated signal, the CM generalized kurtosis algorithm is deduced.

并结合调制信号的恒模特性，提出了基于广义峭度的恒模盲信号提取算法。

4.The sorting of the aeolian sand soil in the middle and southern desert is medium and better and the skewness is near symmetrical and the kurtosis is leptokurtic.

中部、南部风沙土分选性中等和较好，呈窄峰态和近对称的分布形式。

5.Particularly, parameters of model can be chosen to match empirically estimated mean, variance, skewness, and kurtosis of the stock return distribution. The model thus has the potential to produce…

特别地，模型的系数可选择得与股票收益分布的实际估计评均值、方差、挠度和峭度相匹配，因而就可能产生出与实际观测的股票收益分布较为一致的期权价格。

6.Then peak index, kurtosis index, impulsion index and tolerance index of the vibration signal are calculated. These parameters represent the characteristic of failures.

随后计算出代表故障特征的振动信号的峰值指标、峭度指标、脉冲指标和裕度指标等参数。

7.The financial analyst reported that the portfolio had a high kurtosis, indicating a higher risk of extreme returns.

金融分析师报告称该投资组合具有高峰度，这表明极端收益的风险较高。

8.Excess kurtosis can indicate the presence of outliers in the dataset.

超出正常范围的峰度可能表明数据集中存在异常值。

9.A normal distribution has a kurtosis of three, which is considered mesokurtic.

正态分布的峰度为三，被认为是中峰态。

10.Researchers often check for kurtosis when analyzing data distributions to assess their characteristics.

研究人员在分析数据分布时通常会检查峰度以评估其特征。

11.In statistics, kurtosis is used to describe the shape of the distribution's tails.

在统计学中，峰度用于描述分布尾部的形状。

作文

In the field of statistics, understanding various measures of data distribution is crucial for analyzing and interpreting data accurately. One such measure that plays a significant role in understanding the shape of a distribution is kurtosis. Kurtosis refers to the degree of peakedness or flatness of a probability distribution compared to a normal distribution. It provides insight into the tails of the distribution, which can be particularly important in fields such as finance, psychology, and quality control.When we talk about kurtosis, we are essentially discussing how much of the data is located in the tails and how extreme the values in those tails can be. A distribution with high kurtosis indicates that data have heavy tails or outliers, meaning there is a higher probability of extreme values occurring. In contrast, a distribution with low kurtosis suggests that the data are light-tailed, indicating fewer extreme values.There are generally three types of kurtosis: mesokurtic, leptokurtic, and platykurtic. A mesokurtic distribution has a kurtosis value similar to that of a normal distribution, which is around 3. A leptokurtic distribution has a kurtosis greater than 3, indicating a sharper peak and heavier tails. This means that while most data points cluster around the mean, there are more extreme outliers. On the other hand, a platykurtic distribution has a kurtosis less than 3, suggesting a flatter peak and lighter tails, indicating fewer outliers.Understanding kurtosis is essential for data analysts and researchers. For instance, in finance, a high kurtosis in asset returns might suggest a higher risk of extreme losses or gains, which could influence investment strategies. Similarly, in psychological testing, a high kurtosis in test scores may indicate that some participants performed exceptionally well or poorly, highlighting the need for further investigation into the factors contributing to these extremes.Moreover, kurtosis can also affect statistical tests. Many statistical tests assume that the data follow a normal distribution. If the kurtosis is significantly different from that of a normal distribution, it may violate these assumptions, leading to incorrect conclusions. Therefore, it is vital for researchers to assess the kurtosis of their data before conducting further analysis.In conclusion, kurtosis is a fundamental concept in statistics that helps us understand the shape and characteristics of data distributions. By analyzing kurtosis, we can gain insights into the presence of outliers and the overall behavior of the data. Whether in finance, psychology, or any other field that relies on data analysis, recognizing the importance of kurtosis can lead to more informed decisions and better interpretations of data. Thus, mastering the concept of kurtosis is essential for anyone involved in data-driven research or analysis.

在统计学领域，理解各种数据分布的度量对于准确分析和解释数据至关重要。其中一个在理解分布形状方面起着重要作用的度量是峰度。峰度指的是与正态分布相比，概率分布的尖峭程度或平坦程度。它提供了对分布尾部的洞察，这在金融、心理学和质量控制等领域尤为重要。当我们谈论峰度时，我们实际上是在讨论数据在尾部的分布情况以及这些尾部值的极端程度。具有高峰度的分布表明数据具有重尾或异常值，这意味着极端值发生的概率更高。相反，具有低峰度的分布则表明数据较轻尾，极端值较少。通常有三种类型的峰度：中峰型、尖峰型和平峰型。中峰型分布的峰度值与正态分布相似，大约为3。尖峰型分布的峰度大于3，表明有更尖的峰和更重的尾部。这意味着大多数数据点集中在均值附近，但存在更多的极端异常值。另一方面，平峰型分布的峰度小于3，表示峰更平坦，尾部更轻，表明异常值较少。理解峰度对数据分析师和研究人员至关重要。例如，在金融领域，资产收益的高峰度可能表明极端损失或收益的风险更高，这可能影响投资策略。同样，在心理测试中，测试分数的高峰度可能表明一些参与者表现极其优秀或糟糕，强调需要进一步调查导致这些极端情况的因素。此外，峰度还可以影响统计检验。许多统计检验假设数据遵循正态分布。如果峰度与正态分布的值显著不同，可能会违反这些假设，从而导致错误的结论。因此，研究人员在进行进一步分析之前评估数据的峰度至关重要。总之，峰度是统计学中的一个基本概念，帮助我们理解数据分布的形状和特征。通过分析峰度，我们可以洞察异常值的存在及数据的整体行为。无论是在金融、心理学还是任何其他依赖数据分析的领域，认识到峰度的重要性都可以导致更明智的决策和更好的数据解释。因此，掌握峰度的概念对于任何参与数据驱动研究或分析的人来说都是必不可少的。