Meningkatkan Ketahanan Wilayah Melalui Estimasi Underreported Data Kejahatan Menggunakan Pendekatan Bayes

https://doi.org/10.22146/jkn.29197

Herlin Venny Johannes(1*), Septiadi Padmadisastra(2), Bertho Tantular(3)

(1) Departemen Statistik, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Padjajaran
(2) Departemen Statistik, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Padjajaran
(3) Departemen Statistik, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Padjajaran
(*) Corresponding Author

Abstract


ABSTRACT

This paper present a study for the number of crime that run into underreporting counts. The purpose of the analysis is to estimate parameter of the model which is the actual number of crime. The model is a mixture of the poisson and the binomial distributions developed by Winkelmann (1996). The parameters of the model are estimated by Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. Determination the convergence of the algorithm using trace plot, autocorrelation plot and ergodic mean plot. In the end, estimator of the parameters of the underreported counts model are the simulation sample mean that calculated from the simulation sample of iteration after burn in period until the last iteration.

ABSTRAK

Penelitian ini mengkaji permodelan data tingkat kejahatan yang mengalami underreporting counts. Tujuan analisis ini adalah untuk menaksir parameter model yaitu banyaknya jumlah tindak kejahatan yang sebenarnya.  Model yang digunakan adalah hasil penggabungan antara distribusi poisson dan distribusi binomial yang dikembengkan oleh Winkelmann (1996). Penaksiran parameter model dilakukan melalui pendekatan bayes dan simulasi Markov Chain Monte Carlo menggunakan algoritma gibbs sampling. Penentuan konvergensi algoritma akan dilakukan melalui trace plot, autocorrelation plot, dan ergodic mean plot. Taksiran parameter model diperoleh dari rata-rata nilai sampel hasil simulasi yang dihitung dari iterasi setelah burn in period sampai dengan iterasi yang terakhir.


Keywords


Underreporting counts;Bayesian; MCMC; Gibbs sampling

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DOI: https://doi.org/10.22146/jkn.29197

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