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Satriya, Andi M Ade
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Pemodelan Indeks Pembangunan Manusia (IPM) Menggunakan Analisis Regresi Probit Christyadi, Santo; Satriya, Andi M Ade; Goejantoro, Rito
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (936.499 KB) | DOI: 10.30872/eksponensial.v11i2.662

Abstract

Ordinal probit regression analysis is non-linear regression analysis that used to find affected independent variables for ordered categorical dependent variable and regression model in this analysis used Normal cumulative distribution function. Parameter estimation in this model used Maximum Likelihood Estimation (MLE) method. This model has been applied to Human Development Index (HDI) in Borneo Island in 2017 case study. HDI is the most important measurement in improving the human development quality in all cities/regencies in Indonesia. Some factors that affected to IPM, they are Life Expectancy (X1), School Expectancy (X2), Spending per Capita (X3), Average School Duration (X4), and Labour Force Participation Rate (X5). Based on research that was performed by researcher, resulted two factors affecting to HDI, those are Life Expetancy and Average School Duration. This model has classification accuracy of 89,29%, APER (Apparent Error Rate) value of 10,71%, and AIC (Akaike Information Criterion) value of 39,75; this model was very good because prediction value is almost approaching to observation value (actual value).
Estimasi Parameter Model Regresi Linier dengan Pendekatan Bayes Katianda, Kristin Rulin; Goejantoro, Rito; Satriya, Andi M Ade
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (740.26 KB) | DOI: 10.30872/eksponensial.v11i2.653

Abstract

Two types of viewpoints in statistics are Frequentist and Bayesian Method. In Bayesian method sees a parameter as a random variable, so the value is not single. Frequentist method that are often used in linear regression are Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE). But along with developments, several studies show the results of modeling that are better at using Bayesian method than the Frequentist method. The data used is Poverty data in 2017 from BPS East Kalimantan. The purpose of this study is to estimate the parameters of the regression model with the Bayesian method on data on the number of poor people and regional domestic products in East Kalimantan Province in 2017. To estimate the parameters of the Bayesian linear regression model it is used by the prior conjugate distribution. Then the markov chain is designed from the posterior distribution with Gibbs Sampler as many as 50.000 iterations and the estimated parameters that are the average of the Gibbs Sampler value are = 0.9149, = 5.462, and = 0.2827. From the Gibbs Sampler values ​​that have been obtained, a density function for each parameter is generated so that the Bayesian confidence interval (credible interval) for estimation is (0.85; 0.9836), (4.484; 6.439) and (0.2694 ; 0,296) for parameters .
Co-Authors Christyadi, Santo Katianda, Kristin Rulin Rito Goejantoro, Rito