# REGIONAL CLASSIFICATION BASED ON MATERNAL MORTALITY RATE USING A ROBUST SEMIPARAMETRIC GEOGRAPHICALLY WEIGHTED POISSON REGRESSION MODEL
> Fitriayu

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_85211156242
Jurnal / Konferensi: Communications in Mathematical Biology and Neuroscience
Tahun terbit: 2024
DOI: https://doi.org/10.28919/cmbn/8783
ISSN: 20522541
Kuartil SJR: Q4
Citations: 3

## Authors
- Fitriayu

## Abstract
The Semiparametric Geographically Weighted Poisson Regression (SGWPR) model advances the GWPR model, combining local and global parameters relative to location. Outliers are sometimes encountered when analyzing data using the GWPR model. These outliers can be identified as they differ significantly from other sample points. Outliers can impact the estimation results, leading to biased parameter estimates. One approach to addressing outliers is the robust M method. This study aims to classify regions based on the parameter estimates of the robust SGWPR model applied to maternal mortality rate data in East Java Province using Tukey's Bisquare weighting. The outcome of this research is the classification of regions based on significant factors influencing maternal mortality rates in East Java Province in 2021.

## Keywords
- Poisson regression
- Semiparametric regression
- Geographically Weighted Regression
- Semiparametric model
- Poisson distribution
- Statistics
- Regression
- Econometrics
- Regression analysis
- Geography
- Generalized linear model
- Computer science
- Mathematics
- Medicine
- Nonparametric statistics
- Population
- Environmental health

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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