Adding MSNBURR-IIa Distribution to MultiBUGS

Authors

  • Eliana Putri Ramadani BPS-Statistics Kapuas Hulu Regency, Putussibau, Indonesia
  • Achmad Syahrul Choir Politeknik Statistika STIS, Jakarta, Indonesia https://orcid.org/0000-0001-7088-0646
  • Anindya Apriliyanti Pravitasari Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung
  • Joynabel Paraguison Phillippine Statistics Authority, Phillippine

DOI:

https://doi.org/10.34123/jurnalasks.v17i2.804

Keywords:

Bayesian, MCMC, MSNBurr-IIa, MultiBUGS, Neo-normal

Abstract

Introduction/Main Objectives: The MSNBurr-IIa distribution is a neo-normal distribution designed to fit right-skewed data better. This article aims to integrate the MSNBurr-IIa distribution into MultiBUGS, thereby enabling Bayesian estimation of its parameters. Background Problems: Markov Chain Monte Carlo (MCMC) is a popular method for Bayesian computations, although its implementation is frequently challenging. MultiBUGS, a statistical tool that uses the BUGS language, is used to make this easier. Novelty: This paper details integrating the MSNBurr-IIa distribution into MultiBUGS, allowing for estimating its parameters. The module's effectiveness is demonstrated through its application on both simulated data and regional economic growth data of Indonesian districts/cities in 2021. Research Methods: The MSNBurr-IIa module was developed using five steps: requirement, design, development, testing, and implementation in simulation and real-world data. It was built with Blackbox Component Builder, an integrated development environment (IDE) for the Component Pascal programming language. Finding/Results: The findings confirm that MultiBUGS, with the MSNBurr-IIa module, successfully estimates the distribution’s parameters across various datasets.

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Published

2025-12-31

How to Cite

Ramadani , E. P., Choir, A. S., Pravitasari , A. A., & Paraguison, J. (2025). Adding MSNBURR-IIa Distribution to MultiBUGS. Jurnal Aplikasi Statistika & Komputasi Statistik, 17(2), 109–130. https://doi.org/10.34123/jurnalasks.v17i2.804