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Geographically Weighted Logistic Regression (GWLR) creates a logistic regression model by incorporating spatial variation through geographical weighting in parameter estimation. GWLR can be employed for investigating the relationship among a variable and its independent variables when the dependent variable has a Bernoulli distribution. In some studies, not only a single individual or experimental unit is observed but also a group of binary samples that have all been treated in the same manner. As a result, data with n total observations can be considered to have a binomial distribution, and the total availability also indicates that the data have a binomial distribution rather than a Poisson distribution or a negative binomial distribution. In the present research, the GWLR model is modified into Geographically Weighted Binomial Logistic Regression (GWBLR). Unlike the GWLR, which only observes one binary sample as the response variable, the GWBLR allows n binary samples as the response variable. The parameter estimations were calculated by maximum likelihood estimation (MLE), and the optimum bandwidth was determined by a fixed Gaussian function based on the least Cross Validation (CV) result. The likelihood ratio test and the Wald test were used to test hypotheses.We created the model using both simulation and real data. The simulation parameter estimations demonstrated high consistency with the generated data. The GWBLR model was applied to real data to investigate the spatial effects of temperature, sunshine, humidity and precipitation on the number of confirmed Covid-19 cases in Indonesia. Temperature, sunshine, humidity and precipitation all had an important effect on confirmed Covid-19 cases, according to the data. [ABSTRACT FROM AUTHOR]

With INDESO, Indonesia has implemented a system for the monitoring and management of its tuna resources. Despite increasing catch, very few is known about the dynamics and real abundance of tuna species in the Indonesian waters and adjacent oceanic regions. The SEAPODYM model was implemented in an operational chain of production for the Indonesian region to simulate tuna spatial dynamics in realtime. This challenging objective imposed developing a global scale model at coarse resolution to provide initial and boundaries conditions of the regional model. A parameter optimization approach was used to provide the best solution fitting several hundreds of thousand catch observations, over a long historical simulation at coarse resolution. Then downscaling method and regional modelling at high resolution (1/12°x day) were validated to produce realtime and forecast on a weekly basis. The architecture of this application, the approach for its parameterization and some key results are presented and discussed. [ABSTRACT FROM AUTHOR]

Gamma distribution is a general type of statistical distribution that can be applied in various fields, mainly when the distribution of data is not symmetrical. When predictor variables also affect positive outcome, then gamma regression plays a role. In many cases, the predictor variables give effect to several responses simultaneously. In this article, we develop a multivariate gamma regression (MGR), which is one type of non-linear regression with response variables that follow a multivariate gamma (MG) distribution. This work also provides the parameter estimation procedure, test statistics, and hypothesis testing for the significance of the parameter, partially and simultaneously. The parameter estimators are obtained using the maximum likelihood estimation (MLE) that is optimized by numerical iteration using the Berndt–Hall–Hall–Hausman (BHHH) algorithm. The simultaneous test for the model's significance is derived using the maximum likelihood ratio test (MLRT), whereas the partial test uses the Wald test. The proposed MGR model is applied to model the three dimensions of the human development index (HDI) with five predictor variables. The unit of observation is regency/municipality in Java, Indonesia, in 2018. The empirical results show that modeling using multiple predictors makes more sense compared to the model when it only employs a single predictor. [ABSTRACT FROM AUTHOR]