Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi
Salsabila, Santi Wahyu
Unknown Affiliation
Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Bayesian Approach in Estimating Parameters of Zero-Inflated Negative Binomial Regression Model Using Cauchy Prior: Simulation Study on Pneumonia Salsabila, Santi Wahyu; Efendi, Achmad; Nurjannah, Nurjannah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33245

Abstract

The Bayesian approach is one of the parameter estimation methods that can be applied to Zero-Inflated Negative Binomial (ZINB) regression analysis. The ZINB regression model is used to analyze over-dispersion data with excess zeros. This study aims to evaluate the performance of ZINB regression parameter estimation using a Bayesian approach with Cauchy prior in pneumonia studies. The analysis is applied to secondary data as well as to simulated data with various scenarios based on different sample sizes and proportions of zero values such that the optimal model can be determined. The results show that ZINB regression models using the Bayesian approach provide stable parameter estimates as sample sizes and proportions of zeros increase. In cases of under-five deaths due to pneumonia, the data often contains many zeros because not all regions report cases. The ZINB model effectively addresses over-dispersion and excess zeros through a combination of negative binomial and zero-inflation models. This provides more accurate modeling results to support policymaking. The Bayesian approach also provides flexibility in integrating prior information and handling small samples, making the ZINB model well suited for health data with rare events and many zeros.
Zero Inflated Negative Binomial (ZINB) Regression: Application to the Pneumonia Study and Simulation under Several Scenarios Salsabila, Santi Wahyu; Efendi, Achmad; Nurjannah, Nurjannah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.32499

Abstract

This study aims at evaluating the performance of Zero Inflated Negative Binomial (ZINB) regression analysis using the Maximum Likelihood Estimation (MLE) approach through simulation study. The research data used are secondary data and simulations. Secondary data was obtained from the Ministry of Health of the Republic of Indonesia in 2023 regarding cases of under-five deaths due to pneumonia with a total of 38 samples. The simulation study is conducted to analyze the performance of ZINB regression based on various sample sizes and proportions of zero values. The results show that the ZINB regression model with the MLE approach produces parameter estimates that tend to be more sensitive to sample size, with improved performance at large sample sizes. Data with a large proportion of zeros reflects high variability as well as the presence of excess zeros, so the ZINB regression model can provide more stable and precise parameter estimates than those with a lower proportion of zeros. Therefore, the ZINB regression model is effective for data with a high proportion of zeros as it fits the characteristics of the data distribution, especially in cases of under-five deaths due to pneumonia.
Co-Authors Achmad Efendi Nurjannah Nurjannah