CD Skripsi

Pemodelan Regresi Logistik Biner Dengan Pendekatan Bayesian Markov Chain Monte Carlo : Kasus Indeks Kedalaman Kemiskinan Di Indonesia Tahun 2021

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ABSTRACT
The Poverty Gap Index (PGI) is the average expenditure gap of each poor population towards the poverty line. This study aims to model PGI data using binary logistic regression with a classical approach using the Maximum Likelihood Estimation (MLE) method and a Bayesian approach using the Markov Chain Monte Carlo (MCMC) method. MCMC is a popular method for obtaining information about the distribution, especially for estimating the posterior distribution in Bayesian inference with the Metropolis-Hasting algorithm. Factors that have a significant influence on the PGI in Indonesia using the Bayesian approach and the classical approach are the same, namely Life Expectancy and per capita expenditure. Based on the results of the classification with training data of 80% and test data of 20%, a classification accuracy of 82.69% was obtained in the Bayesian approach, whereas with the classical approach, a classification accuracy of 78.84% was obtained. The Bayesian approach is better than the classical approach because the accuracy value in the Bayesian approach is better than the classical approach.
Keywords: PGI, Binary Logistic Regression, MLE, Bayesian, MCMC, Metropolis-Hasting Algorithm, and Classification.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

1903113006

Penerbit

Pekanbaru : Universitas Riau – FMIPA – Statistika., 2023

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

1903113006

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

STATISTIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

TIAR

Versi lain/terkait

Lampiran Berkas

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