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Updated: Jun 12, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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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
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

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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.