Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal ESTIMASI: Journal of Statistics and Its Application
Anisa Kalondeng
Unknown Affiliation
Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pemodelan Regresi Bivariate Poisson Inverse Gaussian pada Kasus Kematian Ibu dan Neonatal di Sulawesi Selatan Nurul Ikhsani; Anisa Kalondeng; Nirwan Ilyas
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24113

Abstract

Overdispersion is a state with a variance value greater than the mean value so the Poisson Inverse Gaussian regression model is used. Meanwhile, to model two correlated response variables, the Bivariate Poisson Inverse Gaussian (BPIG) regression model was used. The BPIG model is a mixed- distributed model between the Poisson Bivariate and Gaussian Inverse distributions. The parameters of the BPIG regression model are estimated using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. This study was applied to data on the number of maternal and neonatal deaths in South Sulawesi in 2019. The results obtained are predictor variables that affect the number of maternal and neonatal deaths in South Sulawesi in 2019, namely K4 services for pregnant women , active birth control participants , handling obstetric complications , handling neonatal complications  and the number of health centers .
Pemodelan Mixed Geographically Weighted Regression yang Mengandung Multikolinearitas dengan Regresi Ridge Suritman; Raupong; Anisa Kalondeng
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.25426

Abstract

In the Mixed Geographically Weighted Regression (MGWR) model, some variables are local and some are global. In MGWR modeling, it is often found that the data have multicollinearity. To overcome this problem, MGWR models with ridge regression are used. The MGWR model can be applied to poverty cases because it can experience spatial heterogeneity due to differences in geographical, cultural, and economic policies that vary in each region. In this study, the estimation of MGWR model parameters with ridge regression is then applied to data on the poor population of South Sulawesi in 2016. Data on the poor population of South Sulawesi experience multicollinearity, so it is solved using the MGWR model with ridge regression. Variables that have a significant effect globally are x3 and x6. while the variables that have a significant local effect are x2, x4, x5, x7, x8, x9 and x10. The AIC value of the MGWR model with ridge regression of 63.64473 is smaller than the MGWR model, meaning that the addition of ridge regression to the MGWR model makes the model better at overcoming multicollinearity problems.
Co-Authors Nirwan Ilyas Nurul Ikhsani Raupong Suritman