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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Developing optimal group acceptance sampling plans based on Weibull distribution with limited risks.

M Naghizadeh Qomi¹, Muhammad Aslam²

¹Department of Statistics, University of Mazandaran, Babolsar, Iran.

Journal of Applied Statistics

|June 4, 2026

Summary

This summary is machine-generated.

This study introduces a new group sampling plan for product quality assessment using time-truncated life tests with Weibull distributions. The proposed plan is more efficient and requires a smaller sample size than traditional methods.

Keywords:

62N05 62P30 Group acceptance sampling plan integer nonlinear programing operating characteristics truncated life test weighted-average of risks

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Area of Science:

Quality Control
Reliability Engineering
Statistical Process Control

Background:

Conventional single and group sampling plans are used for product quality assessment.
Group sampling plans offer improved efficiency in cost and time compared to single sampling plans.
Life testing is crucial for evaluating product durability and reliability.

Purpose of the Study:

To develop an efficient group sampling plan for lot acceptance in time-truncated life tests.
To optimize sampling plans for products with Weibull distributed lifetimes.
To minimize both producer and consumer risks in quality assessment.

Main Methods:

Development of a group acceptance sampling plan (GASP) for time-truncated life testing.
Utilizing the Weibull distribution to model product lifetimes.
Employing integer nonlinear programming to determine optimal group and acceptance numbers.
Minimizing and limiting weighted averages of producer and consumer risks.

Main Results:

The proposed group sampling plan (GASP) demonstrates superior performance compared to traditional optimal two-point plans.
The GASP requires a significantly smaller sample size, enhancing efficiency.
Analysis of tables and figures illustrates the behavior and effectiveness of the proposed plans.
Real data analysis validates the practical applicability of the developed sampling plan.

Conclusions:

The developed group sampling plan is an effective and efficient method for lot acceptance in time-truncated life tests.
The proposed plan offers advantages in terms of sample size reduction over existing methods.
The methodology is robust and applicable to various lifetime distribution models, including the Weibull distribution.