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

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
Censoring Survival Data01:09

Censoring Survival Data

529
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
529
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

398
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.
398
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
Stresses under Combined Loadings01:23

Stresses under Combined Loadings

457
When analyzing a bent tube with a circular cross-section subjected to multiple forces, it is crucial to determine the stress distribution in order to maintain structural integrity under varied load conditions.
The process begins by slicing the tube at critical points and analyzing the internal forces and stress components at these sections, focusing on the centroid. Normal stresses, generated by axial forces and bending moments, are either compressive or tensile and vary across the section from...
457
Stress: General Loading Conditions01:15

Stress: General Loading Conditions

530
To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes....
530

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

Circulating levels of leptin in different phases of the menstrual cycle - A systematic review and meta-analysis.

Journal of family medicine and primary care·2026
Same author

Design and optimization of electro-functional polymeric components exercising entropy-weighted TOPSIS method.

Scientific reports·2026
Same author

Minute-level ECG-derived autonomic dynamics around obstructive apnea events: A peri-event analysis of the PhysioNet apnea-ECG database.

Clinical physiology and functional imaging·2026
Same author

Concordance between different tools monitoring progression in interstitial lung diseases at 6 months-A prospective observational study.

Lung India : official organ of Indian Chest Society·2026
Same author

Laplace Transform-Based Nonparametric Test of Exponentiality against DMRL class with preservation under the Homogeneous Poisson Shock Model and applications in survival analysis and reliability.

PloS one·2026
Same author

Comparative Analysis of Peer-Assisted Jigsaw Teaching Strategy vs Conventional Didactic Teaching With and Without ScholarRx Utilities: A Four-armed Randomized Trial in Medical Education (JIGSAW-Rx Trial).

Medical science educator·2026

相关实验视频

Updated: Jan 17, 2026

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

10.8K

在逐步审查的物流指数模型的多元件应力强度可靠性上.

Amulya Kumar Mahto1, Kumar Abhishek2, Yogesh Mani Tripathi2

  • 1Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, Assam 781039, India.

Alexandria engineering journal AEJ
|September 17, 2025
PubMed
概括

本研究估计了使用逐步审查数据的多元组件应力强度 (MSS) 模型的可靠性. 它引入了物流指数分布的新方法,为复杂的产品提供了改进的可靠性评估.

关键词:
62F10 它们是什么?62F15 一个很好的例子62N02 它们是什么?贝叶斯估计是贝叶斯的估计.值得信赖的时间间隔.林德利的近似方法最大的概率估计估计.一个渐进式的审查计划.

更多相关视频

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine
05:56

Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine

Published on: October 27, 2023

1.7K

相关实验视频

Last Updated: Jan 17, 2026

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

10.8K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.5K
Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine
05:56

Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine

Published on: October 27, 2023

1.7K

科学领域:

  • 工程 工程师 工程师 工程师
  • 统计 统计 统计 统计
  • 可靠性工程可靠性工程

背景情况:

  • 现代技术的进步导致了复杂的多元组件产品.
  • 在市场推出之前评估这些产品的可靠性是一项挑战.
  • 可靠性估计对于产品质量和消费者安全至关重要.

研究的目的:

  • 开发和评估多元件应力强度 (MSS) 模型可靠性估计的方法.
  • 将这些方法应用到使用后勤指数分布逐步审查的数据上.
  • 为了比较MSS可靠性的经典和贝叶斯估计技术.

主要方法:

  • 使用经典和贝叶斯估计程序.
  • 用后勤指数分布建模组件故障.
  • 使用逐步审查的数据进行可靠性分析.
  • 导出最大概率估计器并构建非对称区间.
  • 在贝叶斯推理中应用林德利近似和马尔科夫链蒙特卡洛 (MCMC).

主要成果:

  • 在不同的场景下 (已知/未知常见形状参数) 导出了MSS可靠性的点和间隔估计器.
  • 贝叶斯可信区间是使用林德利近似和MCMC方法构建的.
  • 一项模拟研究证明了拟议估计器的性能.
  • 这些方法用现实世界的数据集来说明.

结论:

  • 该研究提供了强大的方法可靠性估计在MSS模型的物流指数分布.
  • 经典和贝叶斯的方法都为产品可靠性提供了有价值的见解.
  • 这些发现适用于需要评估复杂产品可靠性的制造商.