How Big Poverty in Central Java: Mixed Regressive-Spatial Autoregressive Models

Mixed Regressive-Spatial Autoregressive Models

  • Rezzy Eko Caraka School of Mathematical Sciences Faculty of Science and Mathematics The National University of Malaysia
DOI: https://doi.org/10.24843/JEKT.2018.v11.i01.p04

Abstract

Mixed Regressive-Spatial Autoregressive Models (MR-SAM) is one spatial model with an area approach that takes into account the spatial influence of lag on the dependent variable. The advantage of this model is we can know the location has spatial effect or not. In this paper uses MR-SAM to determine and analyze the factors that affect the category of the poor in Central Java. MR-SAM is one of parametric regression, before using the model we must fulfill assumptions. In a nutshell, at significant ?=5% number of poverty in central java can be explained (statistically significant) by GDP, number of people didn’t finish primary school, and number of people who didn’t finished high school.

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Author Biography

Rezzy Eko Caraka, School of Mathematical Sciences Faculty of Science and Mathematics The National University of Malaysia

My primary research interest lies in the statistics parametric and non-parametric approach both from experimental as well as theoretical points of view and application using the software. I study these aspects using Matlab, R software, STATA, SAS,  WEKA,  STATPLUS, E-Views, Ox Metrics and other tools. These tools allow me to understand broader themes such as application and fundamental approach. As a statistician and data scientist, I work from an intradisciplinary perspective  such as finance, actuarial, bioinformatics, spatial, nutrition, logistics and so on. At the same time,  received research grants . I sometimes think it is important to comment on philosophical matters related to the application of statistics in many areas and to articulate justifications for one side of a particular scientific debate. How to choose relevant methods and suit with the data. Currently, I am working as lecturer at Department of Statistics Padjadjaran University, research assistant at  School of Mathematical Sciences The National University of Malaysia, Bioinformatics and Data Science Research Center Bina Nusantara University. At the same time, adjunct research assistantSchool of Mathematics Clemson University, USA. What’s more, I researched with Technical Committee on Food and Agriculture National Research Council Indonesia (DRN) and during the project development and implementation. I passed technical training in intermediate level data science by Deep Learning Institute NVIDIA.

References

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Published
2018-02-27
How to Cite
CARAKA, Rezzy Eko. How Big Poverty in Central Java: Mixed Regressive-Spatial Autoregressive Models. Jurnal Ekonomi Kuantitatif Terapan, [S.l.], p. 53-60, feb. 2018. ISSN 2303-0186. Available at: <https://ojs.unud.ac.id/index.php/jekt/article/view/37925>. Date accessed: 02 nov. 2024. doi: https://doi.org/10.24843/JEKT.2018.v11.i01.p04.
Citation Formats
Issue
2018: Vol. 11, No.1, Februari 2018 (pp. 1-144)
Section
Articles
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