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GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM

Aliyah Husnun Azizah  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
*Nurjannah Nurjannah  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Adji Achmad Rinaldo Fernandes  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Rosita Hamdan  -  Department of Development Economics, University Malaysia Serawak, Malaysia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.
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Keywords: Geographically Weighted Regression; Geographically Weighted Panel Logistic Regression Semiparametric; Poverty Gap Index.

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