PERBANDINGAN REGRESI BINOMIAL NEGATIF DAN REGRESI GENERALISASI POISSON DALAM MENGATASI OVERDISPERSI (Studi Kasus: Jumlah Tenaga Kerja Usaha Pencetak Genteng di Br. Dukuh, Desa Pejaten)

Keswari, Ni Made Rara; Sumarjaya, I Wayan; Suciptawati, Ni Luh Putu

doi:10.24843/mtk.2014.v03.i03.p072

Cited by 6 publications

(7 citation statements)

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“…3. Coefficient 𝑋 3 is the percentage of school enrollment rate (19)(20)(21)(22)(23)(24) years where every 1% increase in the school enrollment rate of a province will cause the average number of extremely poor people in a province to be exp (-0.01) = 0.99 times the initial value with the assumption that other variables are considered constant. This result is supported by research conducted by [24] that a negative relationship exists between school enrollment and poverty.…”

Section: Negative Binomial Regression Modelmentioning

confidence: 99%

Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia

Santi,

Rahayuningsih

2023

Pattimura Int. J. Math.

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Indonesia's extreme poverty status in 2021 was recorded to be high at 4% or 10.86 million people. One of the efforts in poverty alleviation is to analyze the factors influencing extreme poverty. Although the number of studies on poverty in Indonesia continues to grow, the findings are inconclusive because they are often discussed qualitatively. This study aimed to analyze the factors that influence extreme poverty in Indonesia using negative binomial regression. The data used was the amount of extreme poverty in 34 provinces of Indonesia as the response variable. Then, the explanatory variables used consist of 8 from the Central Bureau of Statistics. The analysis stage sought data exploration, the correlation between variables, Poisson regression model specification and assumption test, handling overdispersion with negative binomial regression, and model feasibility test. Based on the AIC value and dispersion ratio, the negative binomial model obtained an AIC value of 920.03 with a dispersion ratio 1.372. It shows that the negative binomial regression model is good enough to model extreme poverty in Indonesia. Furthermore, the factors significantly influencing extreme poverty in Indonesia are households with proper drinking water, housing status, and families with access to appropriate sanitation.

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Section: Negative Binomial Regression Modelmentioning

confidence: 99%

Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia

Santi,

Rahayuningsih

2023

Pattimura Int. J. Math.

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“…A negative Binomial is a combination of Poisson-Gamma distribution. The presence of Gamma distribution can accommodate overdispersion at Poisson Regression because it does not assume equidispersion in its application [13]. The negative Binomial Regression model is also presented as equation (1).…”

Section: Negative Binomial Regressionmentioning

confidence: 99%

Geographically Weighted Negative Binomial Regression Modeling using Adaptive Kernel on the Number of Maternal Deaths during Childbirth

Fauwziyah¹,

Astutik²,

Pramoedyo³

2022

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The standard model that is used for count data is Poisson Regression. In fact, most of the count data is overdispersed, which means that the response variable has greater variance than the mean. So the Poisson Regression cannot be used because overdispersion can cause inaccurate parameter estimators. One of the most widely used methods to overcome overdispersion is Negative Binomial Regression. If there are spatial effects such as spatial heterogeneity that are taken into Negative Binomial model, the appropriate method to analyze is Geographically Weighted Negative Binomial Regression (GWNBR). A spatial weighting matrix is required in the GWNBR model. In this study, three weighting functions were used, that is Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. From the three weighting functions, a model will be formed and the best model will be selected based on the smallest AIC. Count data used in this study is maternal deaths during childbirth in West Java Province, which is the highest case in Indonesia. The results of the analysis show that based on the smallest AIC, the best modeling in maternal deaths during childbirth in West Java is the GWNBR model using the Adaptive Gaussian Kernel weight. The results of the best model were obtained from three groups based on the predictor variables that had a significant effect.

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“…Distribusi Binomial Negatif merupakan perluasan dari distribusi Poisson-Gamma yang memuat parameter dispersi k. Regresi binomial negatif merupakan suatu model regresi yang digunakan untuk menganalisis hubungan antara sebuah variabel dependen dengan satu atau lebih variabel independen yang mengalami keadaan overdispersi. Regresi Binomial Negatif biasanya digunakan untuk memodelkan data dengan variabel dependen berupa count data dan menggunakan Generalized Linear Model (GLM) dengan fungsi penghubung log [7]. Sehingga, berdasarkan [8] model regresi Binomial Negatif berganda dapat dituliskan sebagai berikut:…”

Section: Regresi Binomial Negatifunclassified

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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PERBANDINGAN REGRESI BINOMIAL NEGATIF DAN REGRESI GENERALISASI POISSON DALAM MENGATASI OVERDISPERSI (Studi Kasus: Jumlah Tenaga Kerja Usaha Pencetak Genteng di Br. Dukuh, Desa Pejaten)

Cited by 6 publications

References 0 publications

Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia

Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia

Geographically Weighted Negative Binomial Regression Modeling using Adaptive Kernel on the Number of Maternal Deaths during Childbirth

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

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