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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

363
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...
363
Probability Laws01:49

Probability Laws

40.1K
Overview
40.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
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...
27
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

97
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
97
Binomial Probability Distribution01:15

Binomial Probability Distribution

10.2K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.2K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

184
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
184

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

Experience of Responding to Imaginative Suggestions: A Micro-Phenomenological Interview Exploratory Study.

The International journal of clinical and experimental hypnosis·2026
Same author

Studying unconscious processing: Contention and consensus.

The Behavioral and brain sciences·2025
Same author

Learners restrict their linguistic generalizations using preemption but not entrenchment: Evidence from artificial-language-learning studies with adults and children.

Psychological review·2024
Same author

The Effects of Linear Order in Category Learning: Some Replications of Ramscar et al. (2010) and Their Implications for Replicating Training Studies.

Cognitive science·2024
Same author

Use one system for all results to avoid contradiction: Advice for using significance tests, equivalence tests, and Bayes factors.

Journal of experimental psychology. Human perception and performance·2024
Same author

Beyond kindness: a proposal for the flourishing of science and scientists alike.

Royal Society open science·2023

相关实验视频

Updated: Jun 5, 2025

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

3.3K

对于物流 (混合效应) 模型的贝叶斯因子.

Catriona Silvey1, Zoltan Dienes2, Elizabeth Wonnacott3

  • 1Division of Psychology and Language Sciences, University College London.

Psychological methods
|December 12, 2024
PubMed
概括
此摘要是机器生成的。

贝叶斯因子可以区分证据缺失的证据,与频率统计不同. 一种新的,简单的方法可以帮助研究人员指定效果大小,提高贝叶斯因子在心理学研究中的可用性.

更多相关视频

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

2.9K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

相关实验视频

Last Updated: Jun 5, 2025

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

3.3K
Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

2.9K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

科学领域:

  • 心理学 心理学 心理学
  • 统计方法 统计方法

背景情况:

  • 心理学中的频率主义统计无法区分证据的缺失和证据的缺失.
  • 贝叶斯因子提供了一个解决方案,但在指定效果大小方面面临挑战,并且具有的学习曲线.

研究的目的:

  • 介绍一种简单的方法,用于生成对二进制依赖变量的可信效应大小范围 (假设1模型).
  • 用一个案例研究和模拟来证明这种方法的实用性.

主要方法:

  • 利用了来自频率主义物流混合效应模型的估计.
  • 采用贝叶斯模型与贝叶斯层次模型进行比较,以提高灵活性.
  • 为第1假设生成了一系列可信的效果大小.

主要成果:

  • 使用提出的方法计算的贝叶斯因子产生了直观合理的结果.
  • 该方法简化了效果大小的规范,解决了贝叶斯因子采用的关键障碍.
  • 在一系列真实效果大小中证明了有效性.

结论:

  • 提出的方法增强了贝叶斯因子在心理学研究中的实际应用.
  • 这种方法有助于对统计证据进行更清晰的解释,特别是在没有影响的情况下.
  • 鼓励更广泛地采用贝叶斯因子来进行更细致的统计推断.