Jove
Visualize
联系我们

相关概念视频

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

125
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...
125
Multiple Bar Graph01:07

Multiple Bar Graph

5.0K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.0K
Functional Classification of Joints01:09

Functional Classification of Joints

3.7K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
3.7K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

5.4K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
5.4K
Variability: Analysis01:11

Variability: Analysis

124
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
124
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

73
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
73

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

Intensity-dependent topographical expansion of sensory representations.

bioRxiv : the preprint server for biology·2026
Same author

Systematic estimates of global causes of neonatal and under 5 mortality in 2000-24: secondary data analysis using bayesian multinomial logistic regression.

BMJ (Clinical research ed.)·2026
Same author

Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.

Allergy·2026
Same author

A Beta-Binomial Model for Estimating Zero- or One-inflated Pain Trajectories.

bioRxiv : the preprint server for biology·2026
Same author

Detection of multiple influential observations on model selection.

Biometrics·2026
Same author

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking.

Environmetrics·2026
Same journal

A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values.

Journal of multivariate analysis·2026
Same journal

Hierarchical structure-guided high-dimensional multi-view clustering.

Journal of multivariate analysis·2026
Same journal

Quadratic inference with dense functional responses.

Journal of multivariate analysis·2025
Same journal

From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.

Journal of multivariate analysis·2024
Same journal

Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data.

Journal of multivariate analysis·2024
Same journal

Nonlinear sufficient dimension reduction for distribution-on-distribution regression.

Journal of multivariate analysis·2024
查看所有相关文章
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

相关实验视频

Updated: May 20, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

多变量函数数据的图形受限分析.

Debangan Dey1, Sudipto Banerjee2, Martin A Lindquist3

  • 1National Institute of Mental Health, Bethesda, 20892, MD, USA.

Journal of multivariate analysis
|March 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于分析复杂功能数据的新方法,确保它尊重已知的变量之间的关系. 这种方法通过保持边际分布,同时保持计算效率来提高准确性,通过神经成像应用程序验证.

关键词:
有条件的独立性 有条件的独立性功能数据分析功能数据分析斯过程是高斯过程.图形模型 图形模型多变量分析多变量分析.空间数据空间数据是指空间数据.

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

966
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

相关实验视频

Last Updated: May 20, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

966
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 神经成像分析分析 神经成像分析

背景情况:

  • 多变量函数数据分析通常需要理解变量之间的条件关系,通常用图形模型表示.
  • 现有的功能高斯图形模型 (GGM) 估计未知的图形,并且无法包含对特定图形结构的先前知识.
  • 预先了解变量间关系在许多应用中至关重要,例如分析已知大脑连接的功能磁共振成像 (fMRI) 数据.

研究的目的:

  • 为多变量函数数据分析提出一种新的方法,严格遵守预定义的图形模型.
  • 建立功能GGM和图形高斯过程 (GP) 之间的理论联系,以利用现有的框架.
  • 开发算法,保留边际分布和计算可扩展性,同时尊重图形约束.

主要方法:

  • 证明了部分可分离的功能GGM和图形GP之间的等价性.
  • 在图形约束下开发了一个使用Dempster的协差选择的新算法,用于在图形约束下进行最大概率估计.
  • 扩展了算法,以解决与低级图形GP近似相关的过度平滑问题,改进了边际分布保存.

主要成果:

  • 建立了功能GGM和图形GP之间的理论联系,使GP可以用于受约束的共变函数构造.
  • 提出了一种算法,有效地将已知的图形结构纳入多变量函数数据的分析中.
  • 与标准低等级近似相比,显示了边际分布的更好的保存,以及计算可扩展性.
  • 通过实证实验和实际的神经成像应用验证了拟议的方法.

结论:

  • 提出的方法提供了一个原则性的方法来分析多变量函数数据,当图形结构是已知的.
  • 开发的算法在尊重已知的图形约束,保持边际分布和保持计算效率之间提供了平衡.
  • 这项工作推进了功能数据分析技术,特别是用于诸如神经成像等可获得先前结构信息的应用.