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

589
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...
589
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

85
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...
85
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

194
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.
194
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

704
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
704
Distribution of Stresses in a Narrow Rectangular Beam01:11

Distribution of Stresses in a Narrow Rectangular Beam

237
In studying beam stress distribution, examining an elemental section is essential. To determine the average shearing stress on this face, the calculated shear is divided by the surface area. Importantly, shearing stresses on the beam's transverse and horizontal planes mirror each other, indicating a consistent stress distribution along the upper region of the beam. Notably, shearing stresses are absent at the beam's upper and lower surfaces due to the absence of applied forces in these...
237
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

100
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
100

You might also read

Related Articles

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

Sort by
Same author

A novel two-sample joint unified hybrid censoring scheme with the application of insulating fluid data.

Journal of applied statistics·2026
Same author

A Model Based on Mixture of Weibull Distributions for Depending Competing Risks Data in the Presence of Long-Term Survivors, and Its Application to Malignant Melanoma Cancer Data.

Statistics in medicine·2026
Same author

Exploring the effects of ROS on PI3K/AKT/mTOR signalling in pediatric low-grade glioma and therapeutic strategies.

Molecular biology reports·2025
Same author

A bivariate load-sharing model.

Journal of applied statistics·2025
Same author

Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene Functionalized via Boron Doped MoS<sub>2</sub> Quantum Dots: A Synergy of Heterojunctions and Doping Effect Enabling Ultrasensitive SO<sub>2</sub> Detection at Room Temperature.

Small (Weinheim an der Bergstrasse, Germany)·2024
Same author

A flexible model based on piecewise linear approximation for the analysis of left truncated right censored data with covariates, and applications to Worcester Heart Attack Study data and Channing House data.

Statistics in medicine·2023

Related Experiment Video

Updated: Sep 8, 2025

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

1.2K

Order restricted classical inference of a Weibull multiple step-stress model.

Ayan Pal1, Sharmishtha Mitra1, Debasis Kundu1

  • 1Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur, India.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

This study introduces a new multiple step-stress model for analyzing product reliability under increasing stress conditions. The model uses Weibull distributions and a tampered failure rate to improve parameter inference for Type-I censored data.

Keywords:
Step-stress modelbootstrap confidence intervalisotonic regressionmaximum-likelihood estimatortampered failure rate based model

More Related Videos

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
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

10.3K

Related Experiment Videos

Last Updated: Sep 8, 2025

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

1.2K
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
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

10.3K

Area of Science:

  • Reliability Engineering
  • Statistical Modeling
  • Accelerated Life Testing

Background:

  • Product lifetime data often requires analysis under varying stress levels to predict reliability.
  • Traditional models may not adequately capture the effects of increasing stress on component failure.
  • Type-I censored data is common in reliability studies, posing unique analytical challenges.

Purpose of the Study:

  • To design and analyze a multiple step-stress model for reliability assessment.
  • To develop order-restricted inference for model parameters using a frequentist approach.
  • To investigate the behavior of lifetime distributions under escalating stress conditions.

Main Methods:

  • Utilized a multiple step-stress model framework.
  • Assumed two-parameter Weibull distributions for lifetime at each stress level.
  • Employed a tampered failure-rate model to link stress levels.
  • Applied frequentist methods for order-restricted parameter inference.
  • Analyzed Type-I censored data.

Main Results:

  • The developed model effectively analyzes reliability under multiple step-stress conditions.
  • Order-restricted inference provides robust parameter estimation for the proposed model.
  • Simulation studies and real data analysis demonstrate the model's practical applicability.

Conclusions:

  • The proposed multiple step-stress model with a tampered failure rate is a valuable tool for reliability analysis.
  • The frequentist approach for order-restricted inference is suitable for this type of reliability data.
  • The study provides a framework for understanding and predicting product lifetimes under accelerated testing scenarios.