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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...

You might also read

Related Articles

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

Sort by
Same author

NIR-II Photosensitizer-Based Nanoparticles Defunctionalizing Mitochondria to Overcome Tumor Self-Defense by Promoting Heat Shock Protein 40.

ACS nano·2025
Same author

Pulmonary microbiota affects silica-induced pulmonary fibrosis through activation of the PI3K/AKT-mediated senescence in alveolar epithelial cells.

Journal of hazardous materials·2025
Same author

Analysis of chemical constituents in different parts of Ribes diacanthum pall using molecular network assistance and their bioactive properties.

Journal of pharmaceutical and biomedical analysis·2025
Same author

Evaluating the "right@home" randomized trial of nurse home visiting using linked administrative data at school transition.

International journal of population data science·2025
Same author

Effect of emulsifier type on camellia oil-based nanostructured lipid carriers for delivery of curcumin.

Food chemistry·2025
Same author

<i>Aloe Vera</i> Gel and Rind-Derived Nanoparticles Mitigate Skin Photoaging via Activation of Nrf2/ARE Pathway.

International journal of nanomedicine·2025
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
Same journal

Adjusted profile likelihood inference for the scale parameter of the Gumbel distribution.

Journal of applied statistics·2026
See all related articles
  1. Home
  3. Inference For Dependent Competing Risks Model Under M-cycle Minimum Ranked Set Sampling.
  1. Home
  3. Inference For Dependent Competing Risks Model Under M-cycle Minimum Ranked Set Sampling.

Related Experiment Video

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

Inference for dependent competing risks model under m-cycle minimum ranked set sampling.

Jiaxin Zhang1, Wenhao Gui1

  • 1School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, People's Republic of China.

Journal of Applied Statistics
|June 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Minimum ranked set sampling optimizes failure time data collection. This study analyzes dependent competing risks using this method, offering new estimation techniques for reliability and risk assessment.

Keywords:
62F1062F1562F3062N05Minimum ranked set samplingdependent competing risksgeneralized inverted exponential distributionorder restriction

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

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

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

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

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Statistics
  • Reliability Engineering
  • Survival Analysis

Background:

  • Minimum ranked set sampling (MRSS) is efficient for collecting failure time data.
  • Dependent competing risks models are crucial for analyzing complex failure mechanisms.

Purpose of the Study:

  • To investigate dependent competing risks within m-cycle MRSS data.
  • To develop and compare Maximum Likelihood Estimation (MLE) and Bayesian inference methods.
  • To assess the impact of sampling strategies on estimation precision.

Main Methods:

  • Utilized the Marshall-Olkin distribution for dependence structures.
  • Applied MLE and Bayesian inference under unrestricted and ordered parameters.
  • Employed Metropolis-Hastings and importance sampling for Bayesian computations.
  • Conducted numerical simulations and analyzed a real-world case study.

    • Main Results:

    • Established conditions for existence and uniqueness of MLEs.
    • Derived interval estimators for model parameters.
    • Obtained posterior estimates using flexible priors.
    • Demonstrated the influence of cyclic sampling on estimation accuracy.

    Conclusions:

    • Provided a comprehensive framework for analyzing dependent competing risks with MRSS data.
    • Offered practical guidelines for implementing these methods in reliability analysis.
    • Highlighted the effectiveness of MRSS in optimizing resource allocation for failure time data collection.