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Meiliyani Siringoringo
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Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123
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Eksponensial
Published by Universitas Mulawarman
ISSN : 20857829     EISSN : 27983455     DOI : https://doi.org/10.30872/
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 12 Documents
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Search results for , issue "Vol. 12 No. 1 (2021)" : 12 Documents clear
Estimasi Parameter Model Regresi Linier Berganda dengan Pendekatan Bayes Menggunakan Prior Pseudo: (Studi Kasus Indeks Pembangunan Manusia (IPM) di Kalimantan Timur) Isgiarahmah, Afryda; Goejantoro, Rito; Nasution, Yuki Novia
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.932 KB) | DOI: 10.30872/eksponensial.v12i1.753

Abstract

The parameter estimation of a regression model can use the Ordinary Least Square (OLS) method which must fulfill the assumption of BLUE. Besides OLS, there is another method that can be used to estimate the regression parameters, namely the Bayes method. Parameter estimates using the OLS method and the Bayes method have been widely used in the field of development. One of them is on economic development, namely the Human Development Index (HDI). The purpose of this study is to know multiple linear regression models and interpretations that state the relationship between per capita expenditure, average length of school, life expectancy, and school length for the Human Development Index (HDI) with the Bayes approach using pseudo priors.
Pencegahan Penyakit Kusta di Lingkungan Hutan Tropis Lembab Kalimantan Melalui Pemodelan Geographically Weighted Poisson Regression Wati, Fatma; Suyitno, Suyitno; Hayati, Memi Nor
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v12i1.756

Abstract

Geographically Weighted Poisson Regression (GWPR) model is a regression model developed from Poisson regression which is applied to spatial data. Parameter estimation of the GWPR model is done at each observation location using spatial weighting. This study goal is to obtain the GWPR model and the factors influencing the number of leprosy cases in each regency(municipality) on Kalimantan Island in 2018. Spatial weighting was obtained by using the adaptive bisquare kernel function and optimal bandwidth was determined by using Generalized Cross-Validation (GCV) criteria. The data of this study was secondary data namely the number of leprosy cases in 56 regency on Kalimantan Island in 2018. The parameter estimation method of GWPR model is Maximum Likelihood Estimation (MLE). The results of analysis showed that maximum likelihood estimator is obtained by using the Newton-Raphson iterative method and the factors affecting the number of leprosy cases in each regency were different and locally. The factors influencing locally were the number of health facilities, the number of health workers, the number of male population and population density.

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