Selvi Mardalena
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Selvi Mardalena has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Hizir Sofyan
Universitas Syiah Kuala
3 shared publications - Rumaisa Kruba
Universitas Syiah Kuala
3 shared publications - Dedy Dwi Prastyo
Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2 shared publications - Jerry Dwi Trijoyo Purnomo
Dept. of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
2 shared publications - Latifah Rahayu
Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Syiah, Kuala, Indonesia
2 shared publications - Latifah Rahayu
1,2,3Statistics Department, Universitas Syiah kuala, Banda Aceh, Indonesia
1 shared publication
Latest Publications
- 1 January 2024
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 3158 (1) , 070008
Dengue fever trends in Aceh using single moving average, weighted moving average and exponential smoothing
Dengue fever is a viral infection that is transmitted through the bite of an infected mosquito. In Aceh, a province located in the northern part of the Indonesi Dengue fever is a viral infection that is transmitted through the bite of an infected mosquito. In Aceh, a province located in the northern part of the Indonesi
- 1 January 2024
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 3158 (1) , 070008
Dengue fever trends in Aceh using single moving average, weighted moving average and exponential smoothing
Dengue fever is a viral infection that is transmitted through the bite of an infected mosquito. In Aceh, a province located in the northern part of the Indonesi Dengue fever is a viral infection that is transmitted through the bite of an infected mosquito. In Aceh, a province located in the northern part of the Indonesi
- 1 January 2024
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 3158 (1) , 020020
Spatial pattern analysis of disaster and its relation to poverty in Indonesia
Natural disasters in Indonesia have significant impacts on poverty, as they often destroy livelihoods and infrastructure, and increase the cost of living. This Natural disasters in Indonesia have significant impacts on poverty, as they often destroy livelihoods and infrastructure, and increase the cost of living. This
- 1 January 2024
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 3158 (1) , 020020
Spatial pattern analysis of disaster and its relation to poverty in Indonesia
Natural disasters in Indonesia have significant impacts on poverty, as they often destroy livelihoods and infrastructure, and increase the cost of living. This Natural disasters in Indonesia have significant impacts on poverty, as they often destroy livelihoods and infrastructure, and increase the cost of living. This
- 1 January 2024
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 3158 (1) , 070009
Wind speed forecasting in Aceh using ARIMA and neural network model
The primary goal of this paper is to offer the wind speed forecast for Banda Aceh, which is located on the Indonesian coast. Wind speed forecasting is significa The primary goal of this paper is to offer the wind speed forecast for Banda Aceh, which is located on the Indonesian coast. Wind speed forecasting is significa
- 1 January 2024
- Published by AIP Publishing in AIP Conference Proceedings
- Vol. 3158 (1) , 070009
Wind speed forecasting in Aceh using ARIMA and neural network model
The primary goal of this paper is to offer the wind speed forecast for Banda Aceh, which is located on the Indonesian coast. Wind speed forecasting is significa The primary goal of this paper is to offer the wind speed forecast for Banda Aceh, which is located on the Indonesian coast. Wind speed forecasting is significa
- Open Access
- 21 April 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (9) , 4199
The Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications
This study aims to develop a method for multivariate spatial overdispersion count data with mixed Poisson distribution, namely the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model. The parameters of the GWMPIGR model are estimated locally using the maximum likelihood estimation (MLE) method by considering spatial effects. Therefore, the significance of the regression parameter differs for each location. In this study, four GWMPIGR models are evaluated based on the exposure variable and the spatial weighting function. We compare the performance of those four models in real-world application using data on the number of infant, under-5 and maternal deaths in East Java in 2019 using five predictor variables. In this study, the GWMPIGR model uses one exposure variable and three exposure variables. Compared to the fixed kernel Gaussian weighting function, the GWMPIGR model with the fixed kernel bisquare weighting function and one exposure variable has a better fit based on the AICc value. Furthermore, according to the best GWMPIGR model, there are several regional groups formed based on predictors that significantly affected each event in East Java in 2019.
- 21 April 2022
- Published by MDPI AG in Applied Sciences
- Vol. 12 (9) , 4199
Open AccessThe Geographically Weighted Multivariate Poisson Inverse Gaussian Regression Model and Its Applications
This study aims to develop a method for multivariate spatial overdispersion count data with mixed Poisson distribution, namely the Geographically Weighted Multivariate Poisson Inverse Gaussian Regression (GWMPIGR) model. The parameters of the GWMPIGR model are estimated locally using the maximum likelihood estimation (MLE) method by considering spatial effects. Therefore, the significance of the regression parameter differs for each location. In this study, four GWMPIGR models are evaluated based on the exposure variable and the spatial weighting function. We compare the performance of those four models in real-world application using data on the number of infant, under-5 and maternal deaths in East Java in 2019 using five predictor variables. In this study, the GWMPIGR model uses one exposure variable and three exposure variables. Compared to the fixed kernel Gaussian weighting function, the GWMPIGR model with the fixed kernel bisquare weighting function and one exposure variable has a better fit based on the AICc value. Furthermore, according to the best GWMPIGR model, there are several regional groups formed based on predictors that significantly affected each event in East Java in 2019.
- Open Access
Parameter Estimation and Hypothesis Testing of Multivariate Poisson Inverse Gaussian Regression
Multivariate Poisson regression is used in order to model two or more count response variables. The Poisson regression has a strict assumption, that is the mean and the variance of response variables are equal (equidispersion). Practically, the variance can be larger than the mean (overdispersion). Thus, a suitable method for modelling these kind of data needs to be developed. One alternative model to overcome the overdispersion issue in the multi-count response variables is the Multivariate Poisson Inverse Gaussian Regression (MPIGR) model, which is extended with an exposure variable. Additionally, a modification of Bessel function that contain factorial functions is proposed in this work to make it computable. The objective of this study is to develop the parameter estimation and hypothesis testing of the MPIGR model. The parameter estimation uses the Maximum Likelihood Estimation (MLE) method, followed by the Newton–Raphson iteration. The hypothesis testing is constructed using the Maximum Likelihood Ratio Test (MLRT) method. The MPIGR model that has been developed is then applied to regress three response variables, i.e., the number of infant mortality, the number of under-five children mortality, and the number of maternal mortality on eight predictors. The unit observation is the cities and municipalities in Java Island, Indonesia. The empirical results show that three response variables that are previously mentioned are significantly affected by all predictors.
Open AccessParameter Estimation and Hypothesis Testing of Multivariate Poisson Inverse Gaussian Regression
Multivariate Poisson regression is used in order to model two or more count response variables. The Poisson regression has a strict assumption, that is the mean and the variance of response variables are equal (equidispersion). Practically, the variance can be larger than the mean (overdispersion). Thus, a suitable method for modelling these kind of data needs to be developed. One alternative model to overcome the overdispersion issue in the multi-count response variables is the Multivariate Poisson Inverse Gaussian Regression (MPIGR) model, which is extended with an exposure variable. Additionally, a modification of Bessel function that contain factorial functions is proposed in this work to make it computable. The objective of this study is to develop the parameter estimation and hypothesis testing of the MPIGR model. The parameter estimation uses the Maximum Likelihood Estimation (MLE) method, followed by the Newton–Raphson iteration. The hypothesis testing is constructed using the Maximum Likelihood Ratio Test (MLRT) method. The MPIGR model that has been developed is then applied to regress three response variables, i.e., the number of infant mortality, the number of under-five children mortality, and the number of maternal mortality on eight predictors. The unit observation is the cities and municipalities in Java Island, Indonesia. The empirical results show that three response variables that are previously mentioned are significantly affected by all predictors.