Jove
Visualize
Contact Us

Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

73
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...
73
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

201
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...
201
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
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...
35
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

114
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.
114
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

392
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...
392
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
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...
47

You might also read

Related Articles

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

Sort by
Same author

Analysis of Weibull time metrics using normal operating via partially accelerated tests with improved adaptive progressive censoring and its applications.

Scientific reports·2025
Same author

Reliability assessment of two production lines using joint progressively type-II censored XLindley samples.

Scientific reports·2025
Same author

Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples.

Scientific reports·2024
Same author

Estimation procedures and optimal censoring schemes for an improved adaptive progressively type-II censored Weibull distribution.

Journal of applied statistics·2024
Same author

E-Bayesian Estimation Using Spacing Function for Inverse Lindley Adaptive Type-I Progressively Censored Samples: Comparative Study with Applications.

Applied bionics and biomechanics·2024
Same author

Inference on Nadarajah-Haghighi distribution with constant stress partially accelerated life tests under progressive type-II censoring.

Journal of applied statistics·2022
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 Experiment Video

Updated: Jun 18, 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

Data analysis with applications in physics and engineering using XLindley model with improved adaptive Type-II

Refah Alotaibi1, Mazen Nassar2,3, Ahmed Elshahhat4

  • 1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Heliyon
|August 5, 2024
PubMed
Summary

This study introduces an improved adaptive Type-II progressive censoring strategy for lengthy trials. Bayesian estimation with likelihood functions is superior for parameter estimation, while Bayesian methods with spacings functions are best for reliability metrics.

Keywords:
Bayesian estimationLikelihood estimationProduct of spacings estimationReliability measuresXLindley distribution

More Related Videos

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

10.7K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.5K

Related Experiment Videos

Last Updated: Jun 18, 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
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

10.7K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.5K

Area of Science:

  • Statistics
  • Reliability Engineering
  • Survival Analysis

Background:

  • Adaptive Type-II progressive censoring is crucial for efficient data collection in long-term trials.
  • The XLindley distribution is a flexible model for analyzing lifetime data.
  • Accurate parameter and reliability estimations are vital for practical applications.

Purpose of the Study:

  • To investigate classical and Bayesian estimation methods for the XLindley distribution under an improved adaptive Type-II progressive censoring scheme.
  • To compare the performance of different estimation techniques for model parameters and reliability metrics.
  • To identify the optimal progressive censoring strategy using optimality criteria.

Main Methods:

  • Employed two classical estimation methods (e.g., Maximum Likelihood Estimation) for point and interval estimations.
  • Utilized Bayesian estimation with a squared error loss function and Markov chain Monte Carlo (MCMC) techniques.
  • Generated Bayes point and credible intervals based on two posterior distribution forms.

Main Results:

  • Simulation studies indicated that Bayesian estimation using the likelihood function outperformed classical methods for parameter estimation.
  • For reliability metrics, Bayesian estimation with the spacings function demonstrated superior performance.
  • Real-world data analysis validated the proposed methods and aided in selecting an optimal censoring strategy.

Conclusions:

  • The Bayesian approach offers significant advantages for estimating parameters and reliability metrics of the XLindley distribution under adaptive Type-II progressive censoring.
  • The choice between likelihood and spacings functions in Bayesian estimation depends on whether parameters or reliability metrics are the primary focus.
  • The study provides practical guidance for implementing advanced censoring strategies and estimation techniques in reliability analysis.