Penerapan Regresi Binomial Negatif Untuk Mengatasi Overdispersi Pada Regresi Poisson

Pradawati, Putu Susan; Sukarsa, I Komang Gde; Srinadi, I Gusti Ayu Made

doi:10.24843/mtk.2013.v02.i02.p031

Cited by 8 publications

(7 citation statements)

References 1 publication

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“…In Poisson regression, ; if it was proven as overdispersion, then the analysis was continued being tested using Negative Binomial Regression (Wulandari, 2015). The final step taken was to determine the best model to determine which model was appropriate in modeling the occurrence of multibacillary leprosy in Surabaya by looking at the AIC value and the log-likelihood value (Pradawati and Sukarsa, 2013).…”

Section: Methodsmentioning

confidence: 99%

Modeling the Number of Multibacillary Leprosy Using Negative Binomial Regression to Overcome Overdispersion in Poisson Regression

Wibisono

Kurniawati

2020

JBK

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Poisson regression is used on discrete data (count) for the formation of the model. There is often a violation in Poisson regression analysis assumptions i.e., overdispersion, which means the average value of the data is smaller than the value of the variance. The number of multibacillary leprosy (MB) in 31 Surabaya districts orderly from 2015 to 2017 has increased as many as 127 cases, 140 cases, and 158 cases. This study aimed to model the number of MB leprosy in Surabaya in 2017 with a Negative Binomial regression in overdispersion. This was quantitative research with a descriptive method that uses secondary data. The data sourced from Surabaya City Health Profile in 2017. The independent variables studied include BCG immunization coverage, the percentage of healthy houses, the percentage of Households with Clean and Healthy Behavior (HCHB), the percentage of the male population, and the population density level. MB leprosy incidence modeling with Poisson regression proved to be overdispersed so that the Negative Binomial regression was used to overcome it. The variable that influenced the MB leprosy incidence with a Negative Binomial regression analysis was the percentage of healthy houses (p = 0.019). MB leprosy occurence will decrease if the percentage of healthy houses increases. The percentage of healthy houses in Surabaya was 86.99%, which increased compared to the previous year with an increase of 1.78%. Public awareness about healthy houses is required to reduce the number of MB leprosy in Surabaya.

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Section: Methodsmentioning

confidence: 99%

Modeling the Number of Multibacillary Leprosy Using Negative Binomial Regression to Overcome Overdispersion in Poisson Regression

Wibisono

Kurniawati

2020

JBK

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“…Likelihod Ratio memiliki statistik uji dimana, = 2(ℓ 1 − ℓ 0 ) dimana ℓ 1 dan ℓ 0 adalah log likelihood masing-masing model pada hipotesis. Statistik uji memiliki distribusi Chi-Square yang asimtotik dengan derajat bebas satu [13].…”

Section: Likelihood Ratio Testunclassified

Aplikasi K-Fold Cross Validation Dalam Penentuan Model Regresi Binomial Negatif Terbaik

Widyaningsih

Arum²,

Prawira³

2021

BAREKENG: J. Il. Mat. & Ter.

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Publikasi ilmiah merupakan salah satu indikator penilaian terhadap kualitas akademisi. Tetapi tidak dapat dipungkiri pembuatan publikasi ilmiah bukanlah suatu hal yang mudah, karena membutuhkan proses pembuatan dan proses penelaahan yang rumit. Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang memengaruhi banyaknya publikasi ilmiah yang dihasilkan oleh mahasiswa PhD Biokimia tahun 1997. Karena variabel dependen merupakan count data, metode analisis yang digunakan adalah Regresi Poisson. Namun karena data mengalami overdispersi, akan digunakan Regresi Binomial Negatif. Perbandingan beberapa model Regresi Poisson dan Binomial Negatif dilakukan untuk menentukan model terbaik dengan k-fold cross validation sebagai validasi model. Hasil penelitian menunjukkan bahwa model terbaik yang didapatkan adalah model Regresi Binomial Negatif dengan variabel independen jenis kelamin, status pernikahan, banyaknya anak dibawah 5 tahun, prestise, dan banyaknya artikel oleh mentor dalam 3 tahun terakhir.

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“…Kondisi overdispersi dapat diketahui dengan melihat nilai deviansi/derajat bebas dan Pearson Chi-Square/bebas keduanya menghasilkan nilai yang lebih besar dari 1. Statistik uji nilai deviansnya sebagai berikut [5]:…”

Section: Uji Overdispersiunclassified

Pemodelan Jumlah Kematian Ibu di Provinsi Sulawesi Selatan Menggunakan Regresi Binomial Negatif

Sauddin¹,

Auliah²,

Alwi³

2020

msa

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Penerapan Regresi Binomial Negatif Untuk Mengatasi Overdispersi Pada Regresi Poisson

Cited by 8 publications

References 1 publication

Modeling the Number of Multibacillary Leprosy Using Negative Binomial Regression to Overcome Overdispersion in Poisson Regression

Modeling the Number of Multibacillary Leprosy Using Negative Binomial Regression to Overcome Overdispersion in Poisson Regression

Aplikasi K-Fold Cross Validation Dalam Penentuan Model Regresi Binomial Negatif Terbaik

Pemodelan Jumlah Kematian Ibu di Provinsi Sulawesi Selatan Menggunakan Regresi Binomial Negatif

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