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All Journal ESTIMASI: Journal of Statistics and Its Application
Angriany, A.Muthiah Nur
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Mathematics

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Pendekatan Zero-Inflated Poisson Inverse Gaussian dalam Pemodelan Kasus Malaria di Puskesmas Kota Makassar Nurhidayah, Fauziah; Raupong, Raupong; Angriany, A.Muthiah Nur
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70561/ejsa.v6i1.43164

Abstract

Poisson regression is one of the approaches used to model count data. However, this method has an assumption of equidispersion that is not always met in actual data. One problem that often arises is overdispersion, especially when there are excess zeros in the dependent variable. The Mixed Poisson method, namely Zero-Inflated Poisson Inverse Gaussian (ZIPIG) regression is one approach that can be used when there is overdispersion in the data.  Parameter estimation in the ZIPIG model is done using the Maximum Likelihood Estimation (MLE) method through Fisher Scoring Algorithm iterations. This study discusses how ZIPIG modeling is used to identify factors that influence the number of malaria cases in Makassar City Health Center in 2021. The results of the analysis show that the independent variables that have a significant effect on the number of malaria cases are the number of family heads with access to proper sanitation facilities (X1) and the presence of public places that meet health requirements  (X2).
Pemodelan Nilai Tukar Rupiah terhadap Dolar AS Menggunakan Ridge Regression dengan Koreksi Autokorelasi Prais Winsten Parapa, Anni Ivoni; Raupong, Raupong; Angriany, A.Muthiah Nur
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.45517

Abstract

Linear regression is a statistical method used to analyze the relationship between dependent and independent variables. Parameter estimation is generally obtained through the Ordinary Least Square (OLS) method, which produces unbiased and efficient estimates. However, the presence of multicollinearity and autocorrelation can render OLS estimates suboptimal. The Ridge Regression method combined with Prais Winsten correction can to produce more accurate parameter estimates than OLS. Unlike the subjective approach in determining the bias constant () through Ridge Trace, this study determines the optimal  value by minimizing the Mean Square Error (MSE). The results show that the Ridge Regression model with Prais Winsten autocorrelation correction has an Adjusted R² of 78% and a Root Mean Square Error (RMSE) of Rp364,389. Four independent variables are found to have a significant effect on the exchange rate of the Indonesian Rupiah against the United States Dollar (USD), namely money supply, interest rate, exports, and imports.
Co-Authors Nurhidayah, Fauziah Parapa, Anni Ivoni Raupong, Raupong