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

Econometric Views (EViews)01:29

Econometric Views (EViews)

562
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
562
Survival Tree01:19

Survival Tree

390
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
390
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

250
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...
250
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.0K
Structural Classification of Joints01:20

Structural Classification of Joints

7.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.0K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

503
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,...
503

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

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

Measurement invariance of the Strengths and Difficulties Questionnaire (SDQ) across age groups in a German representative sample: An application of confirmatory factor analysis using k-fold cross-validation.

Psychological assessment·2026
Same author

Beyond the cross-section: Rethinking the intention-behaviour gap through a conceptual and methodological lens.

British journal of health psychology·2025
Same author

Electrophysiological resting-state signatures link polygenic scores to general intelligence.

Scientific reports·2025
Same author

Antibacterial Polymers Based on Two Orthogonal Binding Motifs Coalesce with Bacterial Matter.

ACS applied bio materials·2025
Same author

Beyond averaging: A transformer approach to decoding event related brain potentials.

NeuroImage·2025
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
Same journal

Best practices in multilevel modeling for within-cluster group comparisons: An evaluation of coding strategies reflecting group composition and heterogeneity.

Psychological methods·2026
Same journal

A unified framework for psychometrics in experimental psychology: The standardized generalized hierarchical factor model.

Psychological methods·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K

连续时间结构方程模型森林.

Pablo F Cáncer1, Manuel Arnold2, Eduardo Estrada3

  • 1UNINPSI Clinical Psychology Center, Department of Psychology, Universidad Pontificia Comillas.

Psychological methods
|June 5, 2025
PubMed
概括
此摘要是机器生成的。

连续时间结构方程模型 (CT-SEM) 森林提供了一种新的方法来分析具有不规则间隔的纵向数据. 这种方法改进了离散时间模型,为变化和动态的个体差异提供了更准确的见解.

更多相关视频

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

6.7K

相关实验视频

Last Updated: Jan 18, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.7K
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

6.7K

科学领域:

  • 纵向数据分析的数据分析.
  • 结构方程建模 结构方程建模
  • 心理测量 心理测量 心理测量

背景情况:

  • 传统的纵向SEM树木/森林使用了离散时间模型.
  • 离散时间模型产生偏差估计与不均间隔的纵向数据.
  • 以前的连续时间SEM (CT-SEM) 实现是计算密集型的,产生了偏差的结果.

研究的目的:

  • 介绍一种新的,计算上可行的CT-SEM森林实现.
  • 解决纵向研究中离散时间模型的局限性.
  • 使用CT-SEM森林研究变化和动态的异质性.

主要方法:

  • 用于CT建模的ctsemOMX组合包.
  • 使用semtree包用于递归分区.
  • 综合得分导向的协变量测试来自结构更改包.
  • 进行了蒙特卡洛模拟研究.
  • 将该方法应用于来自欧洲健康,衰老和退休调查的经验数据.

主要成果:

  • 在计算上,CT-SEM森林的新实施是可行的.
  • 该方法有效地处理纵向数据中的不规则采样方案.
  • 在现实数据上展示了CT-SEM森林的实用性.

结论:

  • CT-SEM森林提供了一种可靠的方法来分析具有不规则时间间隔的纵向数据.
  • 这种方法增强了对发展轨迹中的个体差异的调查.
  • 为研究人员研究复杂数据集的变化和动态提供了有价值的工具.