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相关概念视频

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Correlation of Experimental Data01:23

Correlation of Experimental Data

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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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One-Way ANOVA01:18

One-Way ANOVA

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One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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相关实验视频

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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在一个具有可能无限方差的通用d-factor模型中测试序列相关性.

Yawen Fan1,2,3, Xiaohui Liu1,2, Ting Luo1,2

  • 1School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, People's Republic of China.

Journal of applied statistics
|July 22, 2024
PubMed
概括

本研究引入了两种新的统计测试,以检测金融时间序列数据中的序列相关性,即使具有像GARCH过程这样的复杂错误结构. 这些测试为金融建模提供可靠的估计和准确的分析.

关键词:
C1212 是一个可靠的方法.C2222 这是一个很好的例子.经验概率 经验概率G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1这是GARCH工艺过程.无限的变量 无限的变量序列相关性 序列相关性权衡的经验概率概率.

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科学领域:

  • 计量经济学 计量经济学
  • 金融时间序列分析分析
  • 统计推理 统计推理

背景情况:

  • 序列相关性可能导致时间序列分析中的估计效率低下或偏差.
  • 财务数据往往表现出复杂的错误结构,例如通用自回归条件异构 (GARCH) 过程.
  • 现有的方法可能会与金融建模中常见的无限方差问题作斗争.

研究的目的:

  • 开发和评估新的统计测试,以在一个具有GARCH错误的一般d-因素模型中进行序列相关性.
  • 为了应对金融时间序列中无限差异所带来的挑战.
  • 为精准的时间序列分析提供强大的工具.

主要方法:

  • 开发两种基于概率的经验测试统计.
  • 在温和条件下进行非对称分析以确定基平方分布.
  • 蒙特卡洛模拟来评估有限样本的性能.

主要成果:

  • 提出的两种经验概率统计都是非对称的奇平方分布.
  • 模拟结果显示,这两项测试的有限样本性能都非常出色.
  • 这些测试有效地处理GARCH错误过程和潜在的无限差异问题.

结论:

  • 提出的基于概率的经验测试对于检测GARCH错误的d-因素模型中的序列相关性是有效的.
  • 这些测试为金融时间序列分析提供了可靠的替代方案,特别是在处理无限差异时.
  • 该研究通过汇率对股票回报分析的影响来强调这些测试的实际重要性.