scholarly journals PERBANDINGAN MODEL REGRESI BINOMIAL NEGATIF BIVARIAT DENGAN MODEL GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL BIVARIAT REGRESSION (GWNBBR) PADA KASUS ANGKA KEMATIAN BAYI DAN KEMATIAN IBU DI JAWA TENGAH

2022 ◽  
Vol 10 (4) ◽  
pp. 488-498
Author(s):  
Yashmine Noor Islami ◽  
Dwi Ispriyanti ◽  
Puspita Kartikasari
Keyword(s):  
Infant Mortality ◽  
Maternal Mortality ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Poor People ◽  
Significant Variable ◽  
Central Java ◽  
Observation Area ◽  
Bivariate Regression

Infant mortality (0-11 months) and maternal mortality (during pregnancy, childbirth, and postpartum) are significant indicators in determining the level of public health. Central Java Province which has 35 regencies/cities is included in the top five regions with the highest number of infant and maternal mortality in Indonesia. The data characteristics of the number of infants and maternal mortality are count data. Therefore, the Poisson Regression method can be used to analyze the factors that influence the number of infants and maternal mortality. In Poisson regression analysis, there must be a fulfilled assumption, called equidispersion. Frequently, the variance of count data is greater than the mean, which is known as the overdispersion. The research, binomial negative bivariate regression is used as a solutions to overcome the problem of overdispersion in poisson regression. This method produce a global model. In reality, the geographical, socio-cultural, and economic conditions of each region will be different. This illustrates the effect of spatial heterogeneity, so it needs to be developed into Geographically Weighted Negative Binomial Bivariate Regression (GWNBBR). The model of GWNBBR provides weighting based on the position or distance from one observation area to another. Significant variables for modeling infant mortality cases included the percentage of obstetric complications treated (X1), the percentage of infants who were exclusively breastfed (X3), and the percentage of poor people (X5). Significant variable for modeling maternal mortality cases is the percentage of poor people (X5). Based on the AIC value, GWNBBR model is better than binomial negatif bivariat regression model because it has a smaller AIC value. 

scholarly journals REGRESI POISSON BIVARIAT DENGAN KOVARIAN MERUPAKAN FUNGSI DARI VARIABEL BEBAS

2018 ◽  
Vol 2 (1) ◽  
pp. 23-34
Author(s):  
Untung Kurniawan
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Infant Mortality ◽  
Maternal Mortality ◽  
Regression Model ◽  
Pregnant Women ◽  
Significant Influence ◽  
Count Data ◽  
Poisson Regression ◽  
Test Method

Poisson regression is a regression model which often used to analyze the count data. In this study, poisson regression has been used bivariate poisson regression where the regression is a method which is used to model a pair of correlated count data with multiple predictor variables. The model is used covariance which has a function of the independent variable. The purposes of this study is obtain parameter estimates, test statistics of bivariate poisson regression, and determine the factors that influence of infant mortality and maternal mortality. The data is used from the infant mortality and maternal mortality in Central Java 2015. Based on the result, the parameter estimation of poisson bivariate regression model using maximum likelihood (MLE) method. The results obtained from the parameter estimation are not close form so it needs to be done by Newton-Raphson iteration method. In testing the hypothesis using the Maximum Likelihood Ratio Test method (MLRT) by comparing the value between likelihood below H0 and likelihood below population. Partial of parameters model λ1 (infant mortality) there are six independent variables that have significant influence, namely, delivery by health personnel (X1), pregnant women carry out the program K4 (X3), pregnant women who get Fe3 tablet (X4), handling obstetric complication (X5), exclusively breastfed infants (X7), and households living a clean and healthy life (X8). While for model λ2 (maternal death) only variable handling of neonatal complication (X6) which have no significant influence to response variable.

scholarly journals MODEL REGRESI POISON BIVARIAT DENGAN KOVARIAN KONSTAN

2018 ◽  
Vol 11 (1) ◽  
pp. 27-38
Author(s):  
Untung Kurniawan
Keyword(s):  
Maximum Likelihood ◽  
Infant Mortality ◽  
Maternal Mortality ◽  
Pregnant Women ◽  
Poisson Regression ◽  
Health Personnel ◽  
Likelihood Estimation ◽  
Likelihood Method ◽  
Breastfed Infants ◽  
Central Java

Bivariate Poisson models are appropriate for modeling paired count data exhibiting correlation. This study aims to estimates the parameters and test hypothesis of bivariate Poisson regression on modeling the number of infant mortality and maternal mortality in Central Java 2015. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Results show that the percentage of births by health personnel, the percentage of pregnant women administered the K4 program, the percentage of pregnant women receiving Fe3 tablets, percentage of exclusively breastfed infants, and percentage of households behaved in a clean and healthy life are significant for the number of infant mortality in Central Java. The variables that have significant effect on maternal mortality are percentage of births by health personnel, percentage of maternal women receiving postpartum health services, and percentage of pregnant women receiving Fe3 tablets. Keywords: Bivariate Poisson Regression, Infant Mortality, Maternal Mortality, Maximum Likelihood Estimation

scholarly journals ANALYSIS OF THE NUMBER INFANT AND MATERNAL MORTALITY IN CENTRAL JAVA INDONESIA USING SPATIAL-POISSON REGRESSION

2018 ◽  
Vol 11 (2) ◽  
pp. 135-145
Author(s):  
Alan Prahutama ◽  
Budi Warsito ◽  
Moch. Abdul Mukid
Keyword(s):  
Infant Mortality ◽  
Poisson Regression ◽  
Average Rate ◽  
Medical Personnel ◽  
Health Facilities ◽  
Household Expenditure ◽  
Poor People ◽  
Central Java ◽  
Space Modeling ◽  
Per Capita

Maternal and infant mortality are one of the most dangerous problems of the community since it can profoundly affect the number and composition of the population. Currently, the government has been taking heed on the attempt of reducing the number of maternal and newborn mortality in Central Java which requires data and information entirely. Poisson regression is a nonlinear regression that is often used to model the relationship between response variables in the form of discrete data with predictor variables in the form of discrete or continuous data. In space analysis, GWPR is one of method in space modeling which can model regional-based regression. It is based on some factors including the number of health facilities, the number of medical personnel, the percentage of deliveries performed with non-medical assistance; the average age of a woman's first marriage; the average education level of married women; average amount of per capita household expenditure; percentage of village status; the average rate of exclusive breastfeeding; percentage of households that have clean water and the percentage of poor people. Based on the analysis, it is revealed that the determinants of maternal and infant mortality in Central Java using Poisson and GWPR models, among others are the number of health facilities, the number of medical personnel, the average number of per capita household expenditure and the percentage of the poor. In the maternal and infant mortality model, the AIC value of GWPR model produces better modeling than Poisson regression. Keywords: Maternal and Infant mortality, Poisson, GWPR

scholarly journals PEMODELAN GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION (GWGPR) PADA KASUS KEMATIAN IBU NIFAS DI JAWA TENGAH

2021 ◽  
Vol 10 (2) ◽  
pp. 259-268
Author(s):  
Wahyu Sabtika ◽  
Alan Prahutama ◽  
Hasbi Yasin
Keyword(s):  
Maternal Mortality ◽  
Poisson Regression ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Mean Value ◽  
Significant Variable ◽  
Spatial Factors ◽  
Generalized Poisson ◽  
Central Java ◽  
Generalized Poisson Regression

Maternal mortality is one indicator to describing prosperity in a country and indicator of women's health. Most of the maternal mortality caused by postpartum maternal mortality. The number of postpastum maternal mortality is events that the probability of the incident is small, where the incident depending on a certain time or in a certain regions with the results of the observation are variable diskrit and between variable independent each other that follows the Poisson distribution, so that the proper statistical method is Poisson regression. However, in Poisson regression model analysis sometimes assumptions can occur violations, where the value of variance is greater than the mean value called overdispersion. Generalized Poisson Regression (GPR) is one model that can be used to handle overdispersion problems. This modeling produces global parameters for all locations (regions), so to overcome this we need a method of statistical modeling with due regard to spatial factors. The analytical method used to determine the factors that influence the number of postpartum maternal mortality in Central Java that have overdispersion and there are spatial factors, is Geographically Weighted Generalized Poisson Regression (GWGPR) using the Maximum Likelihood Estimation method and Adaptive Bisquare weighting. Poisson regression and GPR modeling produces a variable percentage of pregnant women doing K1 which has a significant effect on the number of postpartum maternal mortality, while for GWGPR modeling is divided into four cluster in all regency/city in Central Java based on the same significant variable. From the comparison of AIC values, it was found that the GWGPR model is better for analyzing postpartum maternal mortality in Central Java because it has the smallest AIC value.Keywords: The Number of Postpartum Maternal Mortality, Overdispersion, Generalized Poisson Regression, Spatial, Geograpically Weighted Generalized Poisson Regression, AIC

scholarly journals GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION UNTUK MENANGANI OVERDISPERSI PADA JUMLAH PENDUDUK MISKIN

2021 ◽  
Vol 10 (4) ◽  
pp. 532-543
Author(s):  
Nova Delvia ◽  
Mustafid Mustafid ◽  
Hasbi Yasin
Keyword(s):  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Weighting Function ◽  
Negative Binomial Regression ◽  
Spatial Effect ◽  
Poor People ◽  
Research Focus ◽  
Binomial Regression ◽  
Life Circumstances

Poverty is a condition that is often associated with needs, difficulties an deficiencies in various life circumstances. The number of poor people in Indonesia increase in 2020. This research focus on modelling the number of poor people in Indonesia using Geographically Weighted Negative Binomial Regression (GWNBR) method. The number of poor people is count data, so analysis used to model the count data is poisson regression.  If there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression method. GWNBR uses a adaptive bisquare kernel for weighting function. GWNBR is better at modelling the number of poor people because it has the smallest AIC value than poisson regression and negative binomial regression. While the GWNBR method obtained 13 groups of province based on significant variables.      

scholarly journals Comparing of Car-Bym, Generalized Poisson dan Negative Binomial on Tuberculosis Data in Banyumas Districs

2021 ◽  
Vol 5 (1) ◽  
pp. 130-140
Author(s):  
Jajang Jajang ◽  
Budi Pratikno ◽  
Mashuri Mashuri
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Count Data ◽  
Degrees Of Freedom ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Poisson Model ◽  
Generalized Poisson ◽  
Central Java ◽  
Area Data

In 2019 the number of people with TB (Tuberculosis) in Banyumas, Central Java, is high (1,910 people have been detected with TB). The number of people infected Tuberculosis (TB) in Banyumas is the count data and it is also the area data. In modeling, the parameter estimation and characteristic of the data need to be considered. Here, we studied comparing Generalized Poisson (GP), negative binomial (NB), and Poisson and CAR.BYM model for TB cases in Banyumas. Here, we use two methods for parameter estimation, maximum likelihood estimation (MLE) and Bayes. The MLE is used for GP and NB models, whereas Bayes is used for Poisson and CAR-BYM. The results showed that Poisson model detected overdispersion where deviance value is 67.38 for 22 degrees of freedom. Therefore, ratio of deviance to degrees of freedom is 3.06 (>1). This indicates that there was overdispersion. The folowing GP, NB, Poisson-Bayes and CAR-BYM are used to modeling TB data in Banyumas and we compare their RMSE. With refer to RMES criteria, we found that CAR-BYM is the best model for modeling TB in Banyumas because its RMSE is smallest.

PEMODELAN DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat)

2021 ◽  
Vol 10 (3) ◽  
pp. 226-236
Author(s):  
Khusnul Khotimah ◽  
Itasia Dina Sulvianti ◽  
Pika Silvianti
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Kernel Weight ◽  
Negative Binomial Regression Model ◽  
West Java ◽  
Binomial Regression ◽  
Spatial Heterogenity

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

Approaches for dealing with various sources of overdispersion in modeling count data: Scale adjustment versus modeling

2015 ◽  
Vol 26 (4) ◽  
pp. 1802-1823 ◽  
Author(s):  
Elizabeth H Payne ◽  
James W Hardin ◽  
Leonard E Egede ◽  
Viswanathan Ramakrishnan ◽  
Anbesaw Selassie ◽  
...  
Keyword(s):  
Error Estimates ◽  
Standard Error ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Rank Order ◽  
Good Practice ◽  
Information Criteria ◽  
Population Heterogeneity ◽  
Count Response

Overdispersion is a common problem in count data. It can occur due to extra population-heterogeneity, omission of key predictors, and outliers. Unless properly handled, this can lead to invalid inference. Our goal is to assess the differential performance of methods for dealing with overdispersion from several sources. We considered six different approaches: unadjusted Poisson regression (Poisson), deviance-scale-adjusted Poisson regression (DS-Poisson), Pearson-scale-adjusted Poisson regression (PS-Poisson), negative-binomial regression (NB), and two generalized linear mixed models (GLMM) with random intercept, log-link and Poisson (Poisson-GLMM) and negative-binomial (NB-GLMM) distributions. To rank order the preference of the models, we used Akaike's information criteria/Bayesian information criteria values, standard error, and 95% confidence-interval coverage of the parameter values. To compare these methods, we used simulated count data with overdispersion of different magnitude from three different sources. Mean of the count response was associated with three predictors. Data from two real-case studies are also analyzed. The simulation results showed that NB and NB-GLMM were preferred for dealing with overdispersion resulting from any of the sources we considered. Poisson and DS-Poisson often produced smaller standard-error estimates than expected, while PS-Poisson conversely produced larger standard-error estimates. Thus, it is good practice to compare several model options to determine the best method of modeling count data.

scholarly journals PEMODELAN POISSON RIDGE REGRESSION (PRR) PADA BANYAK KEMATIAN BAYI DI JAWA TENGAH

2020 ◽  
Vol 4 (2) ◽  
pp. 392-400
Author(s):  
Wulandari Wulandari
Keyword(s):  
Infant Mortality ◽  
Ridge Regression ◽  
Poisson Regression ◽  
Health Sector ◽  
Predictor Variables ◽  
Poisson Regression Model ◽  
Infant Deaths ◽  
Central Java ◽  
The Government ◽  
Positive Effect

The decline of infant mortality is one of the targets of the Indonesian government in the health sector, including the Government of Central Java. To achieve this goal, it is necessary to identify factors that affect many infant mortalities in the district/city of Central Java. Infant mortalities are count data, so Poisson regression is commonly used. The data in the study showed the existence of multicollinearity in several predictor variables, so an appropriate model was needed. Poisson Ridge Regression (PRR) is a Poisson modeling that accommodates multicollinearity. In this study, the PRR model was used to model infant mortality in Central Java district/city. The results showed that the parameter estimation of the PRR model was slightly different than the estimated Poisson regression model. Modeling infant mortality with the PRR model, out of five predictor variables, three variables harmed many infant deaths, while the other two variables had a positive effect on many infant deaths.

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