scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI KASUS DBD DI SULAWESI SELATAN DENGAN MENGGUNAKAN REGRESI POISSON INVERSE GAUSSIAN

2021 ◽  
Author(s):  
SYAMSUL ALAM
Keyword(s):  
Poisson Distribution ◽  
Inverse Gaussian ◽  
Mixed Poisson Distribution ◽  
Close Form

Regresi Poisson digunakan untuk memodelkan data yang bersifat cacahan seperti data jumlah kasus DBD. Model ini memiliki asumsi yang dipenuhi ialah rata-rata dan variansinya harus memiliki nilai yang sama besar atau disebut equidispersi. Tapi asumsi tersebut tidak terpenuhi karena data jumlah kasus DBD mengalami pelanggaran Asumsi ini. Pelanggarannya ialah nilai rata-rata lebih kecil dari nilai variansi atau disebut overdispersi. Hal ini mengakibabkan kesimpulan yang diperoleh tidak benar karena pendugaan standar error mengalami underestimate. Cara untuk mencegahnya yaitu dengan menggabungkan antara distribusi poisson dan distribusi diskrit atau kontinu, penggabungan ini dinamakan Mixed Poisson Distribution. Peneliti menggunakan metode salah satu dari Mixed Poisson yaitu Regresi Poisson Inverse Gaussian (PIG) karena metode digunakan apabila data tersebut mengalami overdispersi dan parameter diketahui atau close form pada fungsi likelihood. Berdasarkan hasil dari penelitian diketahui bahwa ketinggian wilayah ialah faktor yang mempengaruhi kasus DBD di Sulawesi Selatan secara signifikan dan diperoleh bentuk model yaitu sebagai berikut:π=exp(5,902-0,0004189 X_2)Kata0Kunci : Kasus DBD;0Regresi Poisson;0Overdispersi;0Regresi Poisson0Inverse Gaussian;

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI KASUS DBD DI SULAWESI SELATAN DENGAN MENGGUNAKAN REGRESI POISSON INVERSE GAUSSIAN

2021 ◽  
Author(s):  
SYAMSUL ALAM
Keyword(s):  
Poisson Distribution ◽  
Poisson Regression ◽  
Likelihood Function ◽  
Continuous Distribution ◽  
Inverse Gaussian ◽  
Average Value ◽  
South Sulawesi ◽  
Mixed Poisson Distribution ◽  
Gaussian Regression ◽  
Close Form

Poisson regression is used to model enumeration data such as data on the number of DHF cases. This model has the assumption that is fulfilled is the average and the variance must have the same value or it is called the equidispersion. But this assumption is not fulfilled because the data on the number of dengue cases experienced violations of this assumption. The violation is that the average value is smaller than the variance value or it is called overdispersion. This results in incorrect conclusions because the prediction standard error is underestimated. The way to prevent this is by combining the Poisson distribution and discrete or continuous distribution, this combination is called Mixed Poisson Distribution. Researchers use one of the Mixed Poisson methods, namely Inverse Gaussian Poisson Regression (PIG) because the method is used when the data is overdispersed and the parameters are known or close form on the likelihood function. Based on the results of the study, it is known that the height of the area is a factor that significantly influences DHF cases in South Sulawesi and the model form is as follows: π=exp(5,902-0,0004189 X_2)Keyword: DHF Cases; Poisson Regression; Overdispersion; Poisson Inverse Gaussian Regression;

The Triassic Explosion(?): a statistical model for extrapolating biodiversity based on the terrestrial Molteno Formation

Paleobiology ◽  
1996 ◽  
Vol 22 (3) ◽  
pp. 318-328 ◽  
Author(s):  
John Anderson ◽  
Heidi Anderson ◽  
Paul Fatti ◽  
Herbert Sichel
Keyword(s):  
South Africa ◽  
Statistical Model ◽  
Poisson Distribution ◽  
Generalized Inverse ◽  
Companion Paper ◽  
Late Triassic ◽  
Inverse Gaussian ◽  
Terrestrial Biosphere ◽  
Frequency Distributions ◽  
Generalized Inverse Gaussian

Fitting the generalized inverse Gaussian-Poisson distribution (GIGP) to observed frequency distributions of taxa from the Late Triassic Molteno Formation of South Africa has yielded estimates of the corresponding preserved biodiversities. Three extrapolations have been made on the basis of the uniquely rich megaflora/insect coassemblages from 100 taphocoenoses: insect species—335 observed, 7740 preserved; vegetative species—206 observed, 667 preserved; gymnosperm ovulate orders—16 observed, 84 preserved. The reliability of the results varies according to the abundance and observed diversity of the taxa. These results, with further estimations in a companion paper of existed diversity (regional, continental and global), hint at Late Triassic floral and faunal richness akin to today. This conflicts with the traditionally held model of an increasing cone of biodiversity through time and suggests a phase of explosive evolution in the Triassic hitherto unsuspected. Application of the GIGP to other well-documented collections from other periods might reveal a pattern of diversity trends offering fundamentally new insights into the evolving terrestrial biosphere.

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