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

Factorial Design02:01

Factorial Design

13.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.1K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

233
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...
233
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

134
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
134
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
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).
2.6K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

178
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,...
178

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

A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi-Cohort Alzheimer's Disease Study.

Statistics in medicine·2026
Same author

Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.

Statistics in medicine·2023
Same author

Fixed-effects inference and tests of correlation for longitudinal functional data.

Statistics in medicine·2022
Same author

Two-stage linked component analysis for joint decomposition of multiple biologically related data sets.

Biostatistics (Oxford, England)·2022
Same author

Functional data analysis for longitudinal data with informative observation times.

Biometrics·2022
Same author

Single-index models with functional connectivity network predictors.

Biostatistics (Oxford, England)·2021
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
查看所有相关文章

相关实验视频

Updated: Jul 17, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

对于多变量函数数据的潜在因子模型.

Ruonan Li1, Luo Xiao1

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.

Biometrics
|September 4, 2023
PubMed
概括
此摘要是机器生成的。

一个新的功能潜伏因子模型简化了多变量函数数据中的复杂依赖关系. 这种方法提供了一种更节和可解释的方式来分析多个功能,用电脑图数据证明了这一点.

关键词:
同变函数的共变函数fPCA fPCA 是一个功能数据 功能数据模型的识别性模型的识别性.处罚的分线线被处罚.

更多相关视频

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

相关实验视频

Last Updated: Jul 17, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

科学领域:

  • 统计 统计 统计 统计
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 多变量数据分析通常涉及变量之间的复杂依赖关系.
  • 传统的潜在因子模型对于多变量数据是有效的,但可能无法完全捕捉功能关系.
  • 分析高维的功能数据需要能够处理复杂的相互依赖的方法.

研究的目的:

  • 为多变量函数数据提出一种新的功能潜伏因子模型.
  • 扩展隐性因子模型的功能,以处理功能数据结构.
  • 为理解复杂的功能依赖提供一个节和可解释的框架.

主要方法:

  • 使用未观察到的随机过程开发一个功能潜伏因子模型.
  • 导出足够的条件,使模型可识别.
  • 通过模拟研究和现实应用进行验证.

主要成果:

  • 拟议的模型有效地描述了多个函数之间的复杂依赖关系.
  • 可识别性条件确保模型的理论稳定性.
  • 该模型在分析电脑图数据方面显示出实际的实用性.

结论:

  • 功能潜伏因子模型为分析多变量函数数据提供了一个强大的工具.
  • 该模型为现有方法提供了更易于解释和节的替代方案.
  • 该方法通过其成功应用于电脑图数据分析来验证.