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

562
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...
562
Censoring Survival Data01:09

Censoring Survival Data

209
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...
209
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

83
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...
83
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

237
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,...
237
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

178
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.
178
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100

You might also read

Related Articles

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

Sort by
Same author

Utilizing unified progressive hybrid censored data in parametric inference under accelerated life tests.

Scientific reports·2025
Same author

Greening concept in inventory system for deteriorating items with preservation investment and price and stock dependent demand via marine predators algorithm.

Scientific reports·2025
Same author

An application of Arctic puffin optimization algorithm of a production model for selling price and green level dependent demand with interval uncertainty.

Scientific reports·2025
Same author

Waste minimization strategies for environmental sustainability analysis of MABAC based on schweizer-sklar prioritized approach for circular bipolar fuzzy systems.

Scientific reports·2025
Same author

New two parameter hybrid estimator for zero inflated negative binomial regression models.

Scientific reports·2025
Same author

Erratum: Measurement of the Sixth-Order Cumulant of Net-Proton Multiplicity Distributions in Au+Au Collisions at sqrt[s_{NN}]=27, 54.4, and 200 GeV at RHIC [Phys. Rev. Lett. 127, 262301 (2021)].

Physical review letters·2025

Related Experiment Video

Updated: Aug 30, 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

3.4K

Expected Bayesian estimation for exponential model based on simple step stress with Type-I hybrid censored data.

M Nagy1, M H Abu-Moussa2, Adel Fahad Alrasheedi1

  • 1Department of Statistics and Operation Research, Faculty of Science, King Saud University.

Mathematical Biosciences and Engineering : MBE
|August 29, 2022
PubMed
Summary

The expected Bayesian (E-Bayesian) estimation method addresses hyper-parameter selection issues in Bayesian analysis. This study applies E-Bayesian methods to step-stress accelerated data, offering a robust alternative for parameter estimation.

Keywords:
Bayesian estimationE-Bayesian estimationType-I hybrid censoringexponential distributionsimple step stress

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Related Experiment Videos

Last Updated: Aug 30, 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

3.4K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Area of Science:

  • Statistics
  • Reliability Engineering

Background:

  • Selecting hyper-parameter values for Bayesian prior distributions presents challenges.
  • The expected Bayesian (E-Bayesian) estimation method offers a solution to these challenges.

Purpose of the Study:

  • To apply the E-Bayesian estimation method to a step-stress acceleration model.
  • To derive distribution parameters using E-Bayesian approaches under Type-I hybrid censored data.
  • To compare E-Bayesian estimates with traditional Bayesian estimates.

Main Methods:

  • Utilized a step-stress acceleration model with Exponential Type-I hybrid censored data.
  • Derived distribution parameters using E-Bayesian estimation.
  • Employed four distinct loss functions for generating Bayesian and E-Bayesian estimators.
  • Investigated three alternative hyper-parameter distributions for E-Bayesian estimation.
  • Conducted simulation studies for comparative analysis.

Main Results:

  • E-Bayesian estimators were generated using various loss functions and hyper-parameter distributions.
  • Simulation results provided a basis for comparing E-Bayesian estimates against other methods.
  • A real-world data example was analyzed to demonstrate the practical application and comparative performance.

Conclusions:

  • The E-Bayesian estimation method provides a viable approach for parameter estimation in reliability studies.
  • The study highlights the effectiveness of E-Bayesian methods in handling challenges associated with hyper-parameter selection.
  • Comparative analysis using simulation and real data validates the utility of the proposed methodology.