Geographically Weighted Multivariate Logistic Regression Model and Its Application
Joint Authors
Fathurahman, M.
Purhadi, M.
Sutikno, M.
Ratnasari, Vita
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This study investigates the geographically weighted multivariate logistic regression (GWMLR) model, parameter estimation, and hypothesis testing procedures.
The GWMLR model is an extension to the multivariate logistic regression (MLR) model, which has dependent variables that follow a multinomial distribution along with parameters associated with the spatial weighting at each location in the study area.
The parameter estimation was done using the maximum likelihood estimation and Newton-Raphson methods, and the maximum likelihood ratio test was used for hypothesis testing of the parameters.
The performance of the GWMLR model was evaluated using a real dataset and it was found to perform better than the MLR model.
American Psychological Association (APA)
Fathurahman, M.& Purhadi, M.& Sutikno, M.& Ratnasari, Vita. 2020. Geographically Weighted Multivariate Logistic Regression Model and Its Application. Abstract and Applied Analysis،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1119959
Modern Language Association (MLA)
Fathurahman, M.…[et al.]. Geographically Weighted Multivariate Logistic Regression Model and Its Application. Abstract and Applied Analysis No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1119959
American Medical Association (AMA)
Fathurahman, M.& Purhadi, M.& Sutikno, M.& Ratnasari, Vita. Geographically Weighted Multivariate Logistic Regression Model and Its Application. Abstract and Applied Analysis. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1119959
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119959