Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

946
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...
946
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

491
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.
491
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Survival Tree01:19

Survival Tree

498
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...
498
Survival Curves01:18

Survival Curves

930
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
930
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.4K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genealogy-based trait association with LOCATER boosts power at loci with allelic heterogeneity.

Genome research·2026
Same author

Genealogy based trait association with LOCATER boosts power at loci with allelic heterogeneity.

medRxiv : the preprint server for health sciences·2025
Same author

Quantitative System Risk Assessment From Incomplete Data With Belief Networks and Pairwise Comparison Elicitation.

Risk analysis : an official publication of the Society for Risk Analysis·2025
Same author

Clade distillation for genome-wide association studies.

Genetics·2025
Same author

Differential behaviour of a risk score for emergency hospital admission by demographics in Scotland-A retrospective study.

PLOS digital health·2024
Same author

Publisher Correction: Development and assessment of a machine learning tool for predicting emergency admission in Scotland.

NPJ digital medicine·2024
Same journal

The Roles of Place Attachment and Geovisualizations on Hurricane Storm Surge Risk Perceptions and Behavioral Intentions.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Scientists' Experiences With Getting Help Making Strategic Communication Choices.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Correction to "Evolutionarily Optimal Risk Aversion".

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Toward Resilient Cross-Regional Emergency Governance: A Demand-Driven and Propagation-Based Evolutionary Cooperation Framework.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Competition and Collaboration in the AI Race: Country-LevelDirectional Evidence for Risk Monitoring and Policy.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Cyber Resilience: Management With Cybersecurity Controls.

Risk analysis : an official publication of the Society for Risk Analysis·2026
See all related articles

Related Experiment Video

Updated: Apr 28, 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

9.9K

Bayesian Inference for Reliability of Systems and Networks Using the Survival Signature.

Louis J M Aslett1, Frank P A Coolen2, Simon P Wilson3

  • 1Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|June 13, 2014
PubMed
Summary
This summary is machine-generated.

The survival signature offers a flexible method for system reliability, especially for complex systems and networks with diverse components. This approach provides a Bayesian framework for quantifying reliability using component failure data.

Keywords:
Bayesian methodsnetworksnonparametricsparametric lifetime distributionssystem reliability

More Related Videos

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

1.7K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

7.7K

Related Experiment Videos

Last Updated: Apr 28, 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

9.9K
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

1.7K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

7.7K

Area of Science:

  • Reliability Engineering
  • Network Analysis
  • Bayesian Statistics

Background:

  • The traditional system signature is limited to systems with homogeneous components.
  • The survival signature extends reliability quantification to systems with multiple component types and networks.
  • Bayesian methods offer a robust framework for incorporating prior knowledge and updating beliefs with data.

Purpose of the Study:

  • To introduce and demonstrate the application of the survival signature for reliability quantification.
  • To extend the use of the survival signature to complex systems and networks.
  • To present a Bayesian approach for system and network reliability analysis using survival signatures.

Main Methods:

  • Utilizing the survival signature concept for reliability modeling.
  • Applying a Bayesian perspective to reliability quantification.
  • Assuming exchangeable data from tested components, including failure and censoring times.
  • Developing both nonparametric and parametric approaches for analysis.

Main Results:

  • The survival signature effectively quantifies the reliability of systems with multiple component types.
  • The Bayesian framework allows for principled incorporation of component test data.
  • Both nonparametric and parametric methods provide viable approaches for reliability estimation.
  • The survival signature is applicable to network reliability problems.

Conclusions:

  • The survival signature is a powerful and versatile tool for system and network reliability analysis.
  • A Bayesian approach enhances the robustness and flexibility of survival signature-based reliability quantification.
  • The presented methods are suitable for analyzing systems with complex component structures and available failure data.