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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

691
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...
691
Censoring Survival Data01:09

Censoring Survival Data

274
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...
274
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

215
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.
215
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

296
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
296
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

429
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...
429
Generalized Hooke's Law01:22

Generalized Hooke's Law

1.8K
The generalized Hooke's Law is a broadened version of Hooke's Law, which extends to all types of stress and in every direction. Consider an isotropic material shaped into a cube subjected to multiaxial loading. In this scenario, normal stresses are exerted along the three coordinate axes. As a result of these stresses, the cubic shape deforms into a rectangular parallelepiped. Despite this deformation, the new shape maintains equal sides, and there is a normal strain in the direction of the...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Median Estimation with Quantile Transformations: Applications to Stratified Two-Phase Sampling.

Entropy (Basel, Switzerland)·2025
Same author

Maximum likelihood inference for multivariate delay differential equation models.

Scientific reports·2025
Same author

A New Model of Discrete-Continuous Bivariate Distribution with Applications to Medical Data.

Computational and mathematical methods in medicine·2022
Same author

Parameter Estimation in Step Stress Partially Accelerated Life Testing under Different Types of Censored Data.

Computational intelligence and neuroscience·2022
Same author

Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis.

Journal of healthcare engineering·2022
Same author

Stress-Strength Reliability for Exponentiated Inverted Weibull Distribution with Application on Breaking of Jute Fiber and Carbon Fibers.

Computational intelligence and neuroscience·2021

Related Experiment Video

Updated: Oct 7, 2025

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.6K

Inferences for Exponentiated Gamma Constant-Stress Partially Accelerated Life Test Model Based on Generalized Type-I

Abdalla Rabie1, Abd-El-Baset A Ahmad2, Thierno Souleymane Barry3

  • 1Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut 71524, Egypt.

Computational Intelligence and Neuroscience
|January 6, 2022
PubMed
Summary

This study explores statistical estimation methods for the exponentiated gamma distribution under accelerated testing. It introduces Bayesian and E-Bayesian approaches for analyzing reliability data, offering improved parameter estimation.

More Related Videos

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

2.2K
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

10.4K

Related Experiment Videos

Last Updated: Oct 7, 2025

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

12.6K
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

2.2K
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

10.4K

Area of Science:

  • Reliability Engineering
  • Statistical Modeling
  • Accelerated Life Testing

Background:

  • Exponentiated gamma distribution (EGD) is crucial for reliability analysis.
  • Generalized Type-I hybrid censoring and constant-stress partially accelerated life test (CSPALT) models present unique data challenges.
  • Accurate parameter estimation is vital for predicting product lifespan and performance.

Purpose of the Study:

  • To investigate Bayesian and E-Bayesian estimation methods for EGD parameters under CSPALT with hybrid censored data.
  • To compare these methods with Maximum Likelihood Estimation (MLE).
  • To evaluate the performance of different loss functions (Squared Error Loss and LINEX) in estimation.

Main Methods:

  • Utilizing the MCMC (Markov Chain Monte Carlo) method for Bayesian and E-Bayesian estimations.
  • Applying Maximum Likelihood Estimation (MLE) for parameter and acceleration factor estimation.
  • Employing Squared Error Loss (SEL) and LINEX loss functions for deriving estimates.

Main Results:

  • The study provides both Bayesian and E-Bayesian estimates for the EGD parameters and acceleration factor.
  • Performance evaluation of the proposed estimation techniques is conducted.
  • A real-world data set is analyzed to demonstrate the practical application of the methods.

Conclusions:

  • The proposed Bayesian and E-Bayesian methods offer viable alternatives for parameter estimation in CSPALT models.
  • The choice of loss function impacts the resulting estimates.
  • The study contributes to more robust reliability analysis under accelerated testing conditions.