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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Eksponensial
Suyitno Suyitno
Laboratorium Statistika Terapan FMIPA Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Journal : Eksponensial

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Penaksiran Parameter Model Mixed Geographically Weighted Regression (MGWR) Data Indeks Pembangunan Manusia di Kalimantan Tahun 2016 Mita Asti Wulandari; Suyitno Suyitno; Wasono Wasono
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (795.642 KB)

Abstract

Mixed Geographical Regression (MGWR) model is a combination of global linear regression model and GWR model. Some MGWR parameters are global (the same value) and the other parameters are local (different values) ​​at each observation location. The purpose of this study is to obtain MGWR model for every District’s HDI and to obtain the factors that significantly influence District HDI in East Kalimantan, Central Kalimantan and South Kalimantan Provinces. Estimating parameters for global parameters use Ordinary Least Square (OLS) method. Estimating parameters for local parameters use Weighted Least Square (WLS) method, where weighting spatial is determined by using gaussian adaptive function. Based on the result of MGWR parameters testing, it was concluded that the school enrollment rates (SMP) affected the HDI of all districs in East Kalimantan, Central Kalimantan and South Kalimantan provinces. The population density and the percentage of poor people influence locally to HDI.
Penaksiran Parameter dan Pengujian Hipotesis Model Regresi Weibull Univariat Suyitno Suyitno
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (509.861 KB) | DOI: 10.30872/eksponensial.v8i2.41

Abstract

In this study, a univariate Weibull regression model is discussed. The Weibull regression is a regression model developed from the Weibull distribution, that is the Weibull distribution depending on the covariates or the regression parameters. The univariate Weibull regression (UWR) model can involve the survival function model and the mean model of the response variable with the scale parameter stated in the terms of the regression parameters. The aim of this study is to estimate the UWR model parameters using the maximum likelihood estimation (MLE) method, and to test the regression parameters. The result shows that the closed form of the maximum likelihood estimator can not be found analytically, and it can be approximed by using the Newton-Raphson iterative method. The regression parameters testing involves simultaneous and partial test. The test statistic for simultaneous test is Wilk's likelihood ratio. Wilk statistic follows Chi-square distribution, which can be derived from the likelihood ratio test (LRT) method. The test statistic for partial test is Wald and it follows standard normal distribution. The alternative test statistik for partial test is squared of Wald statistic, where it follows Chi-square distribution with one degree of freedom.
Co-Authors Adelia Miranda Ineu Sintia Meirinda Fauziyah Memi Nor Hayati Mita Asti Wulandari Nur - Azizah Wasono Wasono