Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mean Absolute Deviation01:13

Mean Absolute Deviation

2.7K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.7K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.9K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

5.4K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
5.4K
Coefficient of Correlation01:12

Coefficient of Correlation

6.4K
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...
6.4K
Sampling Distribution01:12

Sampling Distribution

13.6K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
13.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Rotation Local Solutions in Multidimensional Item Response Theory Models.

Educational and psychological measurement·2024
Same author

Make Some Noise: Generating Data from Imperfect Factor Models.

Multivariate behavioral research·2024
Same author

Local Minima in Multidimensional Item Response Theory Models.

Multivariate behavioral research·2023
Same author

Multi-Battery Factor Analysis in R.

Applied psychological measurement·2022
Same author

Local Minima in Factor Rotations.

Multivariate behavioral research·2022
Same author

Heywood You Believe It? Heywood Cases Can Occur When Factor Analyzing Population-Level Dispersion Matrices with Model Error.

Multivariate behavioral research·2022
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
查看所有相关文章

相关实验视频

Updated: Sep 13, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

如何获得MAD:生成均采样的相关性矩阵,具有固定的平均绝对差异.

Niels G Waller1

  • 1Department of Psychology, University of Minnesota, Minneapolis, MN, USA.

Multivariate behavioral research
|August 4, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种快速算法,用于生成有控制错误的相关矩阵. 这种方法对于模型稳定性,投资组合压力测试和分析模型近似误差是有用的.

关键词:
相关性矩阵的相关性矩阵.蒙特卡洛研究的研究.圆形的几何结构是圆形的几何结构.这意味着绝对差异的平均值.

更多相关视频

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

7.2K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.5K

相关实验视频

Last Updated: Sep 13, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

7.2K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.5K

科学领域:

  • 统计 统计 统计 统计
  • 计算数学 计算数学 计算数学
  • 金融建模金融建模

背景情况:

  • 产生具有特定属性的相关矩阵对于各种统计和金融应用至关重要.
  • 现有的方法可能缺乏效率或控制与目标矩阵偏差的程度.
  • 了解模型近似误差是稳健分析和压力测试的关键.

研究的目的:

  • 介绍一种简单快速的算法,用于生成与目标人口矩阵 (R_pop) 相对的固定平均绝对差异 (MAD) 的均采样相关性矩阵 (R).
  • 为了证明这个算法的实用性在应用中,例如模型稳定性研究,投资组合压力测试和动态模型合适分析.
  • 用更高维度几何学来描述算法生成的矩阵的属性,提供理论基础.

主要方法:

  • 开发一种新的算法,用于用受控的MAD采样相关性矩阵.
  • 应用高维几何来定义具有固定 MAD 的矩阵所在的数学空间.
  • 使用R代码实现算法,并附有可复制性的材料.

主要成果:

  • 该算法有效地生成与指定的MAD相关性矩阵.
  • 具有固定 MAD 的矩阵被证明位于圆形和交叉多边形的交叉点上.
  • 对于n=3,这些几何集合可以被视为圆四面体和八面体表面.

结论:

  • 开发的算法为生成相关矩阵提供了一个实用的工具,可以精确控制近似误差.
  • 该理论框架提供了对有边界差异的相关矩阵的几何结构的见解.
  • 该算法及其实现对统计,金融和计算建模领域的研究人员和从业人员来说是有价值的.