![]() Therefore, in this study we apply iterative procedure by the Newton-Raphson algorithm. ![]() This method cannot find an analytical solution since the first derivatives of the log-likelihood function is not available in closed form. In this paper is studied the problem of parameters estimation in GWMPR model by using Maximum Likelihood Estimation ( MLE ) method. The locations is expressed as a point coordinate in two-dimensional geographic space (latitude and longitude). The underlying idea of GWMPR model is that for each estimator of the regression parameters depend on the location where the data are observed. Geographically Weighted Multivariate Poisson Regression (GWMPR) is a statistical technique on spatial data which is used to modelling of relationships between two or more response variables (Poisson distribution) and one or more independent variables. Factors that significantly affect the risk of pneumonia in toddlers in East Java Province is the percentage of low birth weight, the percentage of toddlers who get measles immunization, the percentage of toddlers who get vitamin A, and the percentage of toddlers who get DPT+HB immunization. ![]() The results showed that there are no significant differences between the logistic regression model with GWLR model. GWLR is a statistical method for analyze the data to account for spatial factor. One of many method to solve the spatial data is Geographically Weighted Logistic Regression (GWLR). The main problem of this method if it’s applied in data that is affected of geographic location or spatial data. Logistic regression is a statistical analysis that is used to describe the response variable is categorical with the independent variables are categorical or continuous. This research is aim to determine the comparison of logistic regression models and models Geographically Weighted Logistic Regression and the factors that significantly affect the risk of pneumonia in toddlers in East Java Province. The results of this study will assist local governments in anticipating the causes of maternal mortality. The results showed that the Akaike Information Criterion Corrected (AICc) value in the GWBZIGPR model is smaller than BZIGPR, so it means that the GWBZIGPR is better than the BZIGPR for modeling the number of pregnant maternal mortality and postpartum maternal mortality in Pekalongan Residency. The data used in this study are the number of pregnant maternal mortality and postpartum maternal mortality data in 91 sub-districts in Pekalongan Residency, Central Java Province. The parameter estimation using the Maximum Likelihood Estimation (MLE) method obtained an equation that did not closed-form so that the numerical iteration of Berndt Hall Hall Hausman (BHHH) is used. The GWBZIGPR produces a local parameter estimator for each location of observation. The extension of the ZIGPR model by considering spatial factor called Geographically Weighted Zero Inflated Generalized Poisson Regression (GWBZIGPR). ![]() This study discusses the development of Zero Inflated Generalized Poisson Regression (ZIGPR) with two response variables, that is Bivariate ZIGPR (BZIGPR). ![]()
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