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相关概念视频

Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Goodness-of-Fit Test01:16

Goodness-of-Fit Test

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The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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相关实验视频

Updated: Jul 25, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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使用随机关联的模糊投资组合选择.

Gumsong Jo1, Hyokil Kim1, Hoyong Kim1

  • 1Department of International Finance, Faculty of Finance, Kim Il Sung University, Taesong District, Pyongyang, Democratic People's Republic of Korea.

Computational economics
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用随机相关性 (FPSMSC) 的模糊投资组合选择模型,用于增强投资策略. FPSMSC模型优化了股票选择以获得更高的回报率和更平稳的风险回报变化,优于现有方法.

关键词:
可信度指标是衡量可信度的指标.有效的边境是有效的模糊投资组合选择模糊的投资组合选择测量可能性 测量可能性随机相关性 随机相关性

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相关实验视频

Last Updated: Jul 25, 2025

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07:35

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科学领域:

  • 金融 金融 金融 金融 金融
  • 计算金融是指计算金融.
  • 投资管理 投资管理

背景情况:

  • 传统的投资组合选择模型在处理模糊和随机不确定性方面存在局限性.
  • 整合模糊逻辑和随机过程对于强大的财务建模至关重要.

研究的目的:

  • 提出使用随机相关性 (FPSMSC) 的新型模糊投资组合选择模型.
  • 通过考虑基于模糊专业知识的未来股票价格变动来增强投资组合优化.
  • 评估FPSMSC模型的性能与现有的投资组合选择方法相比.

主要方法:

  • 使用随机相关性 (FPSMSC) 开发了一个模糊的投资组合选择模型.
  • 优化的投资权重使用18个标普500股的月度回报数据 (2011年10月至2015年9月).
  • 使用培训和样本外数据验证模型性能,比较回报率和风险回报平滑度.

主要成果:

  • 与模糊和统计模型相比,FPSMSC模型在各种风险水平上实现了更高的回报.
  • 在涉及风险厌恶参数 (λ) 的回报变化中表现出优越的平滑性.
  • FPSMSC在0-0.3风险厌恶水平中表现特别强,这表明寻求高回报的投资者的效率.

结论:

  • 拟议的FPSMSC模型有效地整合了模糊和随机元素,以改善投资组合选择.
  • 对于投资者来说,FPSMSC提供了一个强大的框架,旨在在风险管理下实现高回报.
  • 该模型能够预测未来的股票走势,并提供更顺的风险回报概况,使其成为投资管理中的一个有价值的工具.