Geographically Weighted Multivariate Logistic Regression Model and Its Application
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 di...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2020/8353481 |
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| _version_ | 1832555058715164672 |
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| author | M. Fathurahman Purhadi Sutikno Vita Ratnasari |
| author_facet | M. Fathurahman Purhadi Sutikno Vita Ratnasari |
| author_sort | M. Fathurahman |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8c4cce881d85464aa5a0c1f560bbd596 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-8c4cce881d85464aa5a0c1f560bbd5962025-02-03T05:49:39ZengWileyAbstract and Applied Analysis1085-33751687-04092020-01-01202010.1155/2020/83534818353481Geographically Weighted Multivariate Logistic Regression Model and Its ApplicationM. Fathurahman0Purhadi1Sutikno2Vita Ratnasari3Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaDepartment of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, IndonesiaThis 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.http://dx.doi.org/10.1155/2020/8353481 |
| spellingShingle | M. Fathurahman Purhadi Sutikno Vita Ratnasari Geographically Weighted Multivariate Logistic Regression Model and Its Application Abstract and Applied Analysis |
| title | Geographically Weighted Multivariate Logistic Regression Model and Its Application |
| title_full | Geographically Weighted Multivariate Logistic Regression Model and Its Application |
| title_fullStr | Geographically Weighted Multivariate Logistic Regression Model and Its Application |
| title_full_unstemmed | Geographically Weighted Multivariate Logistic Regression Model and Its Application |
| title_short | Geographically Weighted Multivariate Logistic Regression Model and Its Application |
| title_sort | geographically weighted multivariate logistic regression model and its application |
| url | http://dx.doi.org/10.1155/2020/8353481 |
| work_keys_str_mv | AT mfathurahman geographicallyweightedmultivariatelogisticregressionmodelanditsapplication AT purhadi geographicallyweightedmultivariatelogisticregressionmodelanditsapplication AT sutikno geographicallyweightedmultivariatelogisticregressionmodelanditsapplication AT vitaratnasari geographicallyweightedmultivariatelogisticregressionmodelanditsapplication |