Work

Control chart for geometrically distributed data based on Bayesian fast double bootstrap

Year: 2025

Type: article

Abstract: Accurate parameter estimation is a critical component of effective process control using g charts. While traditional methods like maximum likelihood and Bayesian estimation are widely used, they may exhibit limitations in small sample size scenarios, leading to inaccurate parameter estimates. To address these challenges, minimum variance unbiased (MVU) estimators have been developed. For specific conditions, such as limited data and no nonconforming items, bootstrap-based Bayesian estimators offer a computational alternative. However, these estimators may struggle to detect significant process shifts, particularly in the presence of large deviations. This research introduces a novel Bayesian fast double bootstrap approach for parameter estimation in g charts. By efficiently handling small sample sizes and effectively detecting large process shifts, this method aims to significantly enhance the accuracy and reliability of process monitoring. The proposed approach leverages the strengths of both bootstrap and double bootstrap techniques, while addressing their limitations through a computationally efficient algorithm. This advancement is expected to contribute to improved process control and quality assurance in various industrial applications.

Source: MethodsX

DOI: 10.1016/j.mex.2025.103307

Authors: Muhammad Yahya Matdoan, Muhammad Mashuri, Muhammad Ahsan

Institutions: Institut Teknologi Sepuluh Nopember, Universitas Pattimura

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,260

Funders: Badan Riset dan Inovasi Nasional, Lembaga Pengelola Dana Pendidikan, Kementerian Keuangan Republik Indonesia