leptokurtic

简明释义

英[ˌlɛp.toʊˈkɜr.tɪk]美[ˌlɛp.toʊˈkɜr.tɪk]

adj. 尖峰态分布的

英英释义

单词用法

同义词

反义词

例句

1.Then the movement feature of the market is analyzed with the GARCH model, and whether the market return follows non-linear and leptokurtic heavy tail feature is validated.

文章还采用GARCH模型对市场的运行特征做了分析，对市场收益率是否服从非线性和尖峰胖尾的特征进行验证。

2.Then the movement feature of the market is analyzed with the GARCH model, and whether the market return follows non-linear and leptokurtic heavy tail feature is validated.

文章还采用GARCH模型对市场的运行特征做了分析，对市场收益率是否服从非线性和尖峰胖尾的特征进行验证。

3.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。

4.The distribution of returns for this investment is considered leptokurtic, indicating a higher peak and fatter tails than a normal distribution.

该投资的收益分布被认为是尖峰厚尾分布，这表明其峰值更高且尾部更粗于正态分布。

5.In finance, a leptokurtic distribution suggests that extreme outcomes are more likely than predicted by a normal distribution.

在金融中，尖峰厚尾分布表明极端结果发生的可能性比正态分布预测的要高。

6.Statisticians often analyze leptokurtic data to understand the risk associated with investments.

统计学家常常分析尖峰厚尾分布数据，以了解与投资相关的风险。

7.The leptokurtic nature of the dataset indicates that we should be cautious about potential outliers.

数据集的尖峰厚尾特性表明我们应该对潜在的离群值保持谨慎。

8.When modeling stock prices, a leptokurtic distribution can provide a better fit than a normal distribution.

在建模股票价格时，尖峰厚尾分布可以比正态分布提供更好的拟合效果。

作文

In the realm of statistics, understanding different types of distributions is crucial for analyzing data accurately. One such distribution is known as leptokurtic, which describes a particular shape of a probability distribution characterized by a higher peak and fatter tails compared to a normal distribution. This means that a leptokurtic distribution has more of its data concentrated around the mean and a greater likelihood of extreme values or outliers. The term 'leptokurtic' comes from the Greek words 'lepto,' meaning thin or slender, and 'kurtos,' meaning curved. Thus, it literally translates to 'slender curve.' When we look at real-world data, we often find that certain phenomena exhibit leptokurtic characteristics. For example, in finance, asset returns can be leptokurtic, indicating that while most returns are close to the average, there is a significant risk of experiencing extreme returns, both positive and negative. This phenomenon is particularly critical for investors as it suggests that traditional risk assessment methods, which assume normal distribution, may underestimate the likelihood of extreme market movements. Understanding whether a dataset is leptokurtic can help statisticians and data analysts make better predictions and decisions. For instance, if a researcher is examining the distribution of test scores in a large class, they might find that the scores are leptokurtic. This could imply that most students scored near the average, but there were also several students who scored exceptionally high or low. Such insights can lead to targeted interventions, allowing educators to provide additional support to those who are struggling or to challenge those who excel. In contrast to leptokurtic distributions, we have platykurtic distributions, which feature flatter peaks and thinner tails. Understanding the difference between these two types of distributions is essential for interpreting data correctly. A platykurtic distribution might suggest that there is less variability in the data, indicating a more uniform performance among participants. Moreover, the concept of leptokurtic has implications beyond academia and finance. In fields such as psychology and sociology, recognizing leptokurtic patterns can aid in understanding behaviors and trends within populations. For example, if survey responses about job satisfaction are leptokurtic, it may indicate that most employees feel moderately satisfied, but there are notable exceptions who either feel extremely satisfied or extremely dissatisfied. This information can be invaluable for organizations aiming to improve employee engagement and retention. In conclusion, the term leptokurtic captures an important aspect of statistical analysis that helps us understand the behavior of data distributions. By recognizing when data is leptokurtic, researchers and analysts can make more informed decisions and predictions, ultimately leading to better outcomes in various fields. Whether in finance, education, or social sciences, the ability to identify and interpret leptokurtic distributions empowers professionals to address challenges and leverage opportunities effectively.

在统计学领域，理解不同类型的分布对于准确分析数据至关重要。其中一个这样的分布被称为leptokurtic，它描述了一种特定的概率分布形状，其特点是比正态分布具有更高的峰值和更胖的尾巴。这意味着leptokurtic分布的数据更集中在均值附近，并且极端值或异常值的可能性更大。‘leptokurtic’这个术语源自希腊语中的‘lepto’，意为细或纤细，以及‘kurtos’，意为弯曲。因此，它字面上的翻译是‘纤细的曲线’。 当我们观察现实世界的数据时，我们常常发现某些现象表现出leptokurtic的特征。例如，在金融领域，资产回报可能是leptokurtic的，表明尽管大多数回报接近平均值，但也有显著的极端回报风险，无论是正向还是负向。这一现象对投资者尤为重要，因为它表明传统的风险评估方法（假设正常分布）可能会低估极端市场波动的可能性。 了解数据集是否为leptokurtic可以帮助统计学家和数据分析师做出更好的预测和决策。例如，如果研究人员正在检查一个大班级的考试成绩分布，他们可能会发现这些成绩是leptokurtic的。这可能意味着大多数学生的成绩接近平均值，但也有几名学生的成绩异常高或异常低。这种洞察可以导致有针对性的干预措施，使教育工作者能够为那些挣扎的学生提供额外支持，或者挑战那些优秀的学生。 与leptokurtic分布相对的是扁平峰（platykurtic）分布，其特点是较平坦的峰和较细的尾。理解这两种类型分布之间的差异对于正确解读数据至关重要。platykurtic分布可能表明数据的变异性较小，暗示参与者之间的表现更加均匀。 此外，leptokurtic的概念在学术界和金融之外也具有影响。在心理学和社会学等领域，识别leptokurtic模式可以帮助理解人群中的行为和趋势。例如，如果关于工作满意度的调查结果是leptokurtic的，这可能表明大多数员工感到适度满意，但也有一些显著的例外，他们要么感到非常满意，要么感到非常不满意。这些信息对于希望提高员工参与度和留任率的组织来说是无价的。 总之，术语leptokurtic捕捉了统计分析中一个重要方面，帮助我们理解数据分布的行为。通过识别何时数据为leptokurtic，研究人员和分析师可以做出更明智的决策和预测，最终在各个领域取得更好的成果。无论是在金融、教育还是社会科学中，识别和解释leptokurtic分布的能力使专业人士能够有效地应对挑战和利用机会。