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All Journal Jurnal TAMBORA Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika
Ni Komang Semara Yanti
Udayana University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics

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ALTERNATIVE MODELS IN OVERCOMING THE PROBLEM OF OVERDISPERSION IN POISSON REGRESSION Pardomuan Robinson Sihombing; Ade Marsinta Arsani; Ni Komang Semara Yanti; Putu Pande Wahyu Diatmika
Jurnal TAMBORA Vol. 7 No. 2 (2023): EDISI 19
Publisher : Wakil Rektor 3, Direktorat Riset, Publikasi dan Inovasi, Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36761/jt.v7i2.2773

Abstract

This study aims to compare various alternative models in overcoming the problem of overdispersion in Poisson regression modeling. The comparative modeling is the Generalized Poisson model, Negative Binomial, and Generalized Negative Binomial. Modeling is applied to modeling the number of poor people in Central Java in 2021 with unemployment, HDI, and GRDP as independent variables. The results obtained by Generalized Poison are better than Negative Binomial and Generalized Negative Binomial because of the smaller AIC and BIC values ??and the larger R2. For simultaneous tests, it can be concluded that unemployment, HDI, and GRDP significantly affect the number of poor people. Only unemployment and HDI variables partially affect the number of poor people in Central Java. On the other hand, there is not enough evidence that GRDP affects some poor people. There is a need for comprehensive and relevant policies to overcome the number of poor people in an area.
IMPLEMENTATION BINARY LOGISTIC MODEL ON FACTORS AFFECTING A PERSON'S SMOKING STATUS Yunita Yunita; Pardomuan Robinson Sihombing; Ade Marsinta Arsani; Ni Komang Semara Yanti; Putu Pande Wahyu Diatmika
Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Vol. 3 No. 2 (2023): Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/bay.v3i2.55

Abstract

This study aims to determine factors influencing a person's smoking status. The independent variables used were gender, working status, marital status, age, and average length of schooling. The data comes from the 2014 Indonesian Family Life Surveys (IFLS). The analytical method used is binomial/binary logistic regression. The results showed that all of the independent variables significantly affected a person's decision to smoke. Partially, age, working status, marital status, and gender positively affect a person's decision to smoke. This result means that at a higher age, a working, married and male person has a greater chance to smoke than a younger, single/not married, and female. On the other hand, the average length of schooling significantly negatively affects smoking, meaning that the higher the education, the lower the chance of smoking. Therefore, regulations that are right on target, both by the government and society, are needed to reduce the number of smokers in Indonesia
KOMPARASI PEMODELAN REGRESI OLS GAUSSIAN, BETA DAN REGRESI FRACTIONAL PADA DATA RASIO Pardomuan Robinson Sihombing; Ade Marsinta Arsani; Ni Komang Semara Yanti; Putu Pande Wahyu Diatmika; Nurhidayati Nurhidayati; Sigit Budiantono
Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Vol. 3 No. 2 (2023): Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/bay.v3i2.61

Abstract

The Public Health Development Index (PHDI) is an indicator that describes health problems and plays a role in efforts to increase long and healthy life expectancy. This study aims to compare PHDI modeling in 35 City Districts of Central Java Province using Gaussian-based regression (OLS), beta regression and fractional regression. The independent variables used are the percentage of poor people and the percentage of households accessing proper sanitation. Data is sourced from the Ministry of Health and Central Java Statistics Agency. All three models gave the same results for both simultaneous and partial tests in modeling PHDI modeling cases. Fractional regression models provide the best results with the smallest error value criteria (AIC and BIC). The percentage of poor people has a significant negative effect on IPKM while the percentage of proper sanitation has a significant positive effect on PHDI. Based on these results, it is expected that policy makers can provide comprehensive and targeted policies in improving PHDI in Indonesia
Co-Authors Ade Marsinta Arsani Ade Marsinta Arsani Ade Marsinta Arsani Nurhidayati Nurhidayati Pardomuan Robinson Sihombing Putu Pande Wahyu Diatmika Sigit Budiantono Yunita Yunita