On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data
The linear models generally rely on the assumption of independence. This assumption is not accomplished in social sciences in which observations are usually correlated. In the case of socioeconomic data, the phenomenon of spatial dependence will lead to the problem of spatial-nonstationarity. There...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IDOSI Publications
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/44211/1/On%20comparison%20between%20logistic%20regression%20and%20geographically%20weighted%20logistic%20regression%20with%20application%20to%20Indonesian%20poverty%20data.pdf |
| _version_ | 1825929505799143424 |
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| author | Saefuddin, Asep Setiabudi, Nur Andi Fitrianto, Anwar |
| author_facet | Saefuddin, Asep Setiabudi, Nur Andi Fitrianto, Anwar |
| author_sort | Saefuddin, Asep |
| collection | UPM |
| description | The linear models generally rely on the assumption of independence. This assumption is not accomplished in social sciences in which observations are usually correlated. In the case of socioeconomic data, the phenomenon of spatial dependence will lead to the problem of spatial-nonstationarity. There are many methods proposed in dealing with spatial-nonstationarity, including geographically weighted regression (GWR), to accommodate local-spatial effect on the observations. The method estimates local parameters in each location rather than single parameter in the global model. GWR can be applied to linear, logistic and Poisson regression. This paper explores properties of GWR for logistic regression, i.e. geographically weighted logistic regression. Application of the model to Indonesia poverty give a contradict results compared to the global logistic model. Statistical tools of model comparison are residuals sum of square, Pearson X², deviance, log likelihood, Akaike information criterion, Bayesian information criterion, spatial autocorrelation coefficient and power of classification accuracy. In general, the logistic GWR performed better than the global one. |
| first_indexed | 2024-03-06T08:57:44Z |
| format | Article |
| id | upm.eprints-44211 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:57:44Z |
| publishDate | 2012 |
| publisher | IDOSI Publications |
| record_format | dspace |
| spelling | upm.eprints-442112020-07-09T07:21:15Z http://psasir.upm.edu.my/id/eprint/44211/ On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data Saefuddin, Asep Setiabudi, Nur Andi Fitrianto, Anwar The linear models generally rely on the assumption of independence. This assumption is not accomplished in social sciences in which observations are usually correlated. In the case of socioeconomic data, the phenomenon of spatial dependence will lead to the problem of spatial-nonstationarity. There are many methods proposed in dealing with spatial-nonstationarity, including geographically weighted regression (GWR), to accommodate local-spatial effect on the observations. The method estimates local parameters in each location rather than single parameter in the global model. GWR can be applied to linear, logistic and Poisson regression. This paper explores properties of GWR for logistic regression, i.e. geographically weighted logistic regression. Application of the model to Indonesia poverty give a contradict results compared to the global logistic model. Statistical tools of model comparison are residuals sum of square, Pearson X², deviance, log likelihood, Akaike information criterion, Bayesian information criterion, spatial autocorrelation coefficient and power of classification accuracy. In general, the logistic GWR performed better than the global one. IDOSI Publications 2012 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/44211/1/On%20comparison%20between%20logistic%20regression%20and%20geographically%20weighted%20logistic%20regression%20with%20application%20to%20Indonesian%20poverty%20data.pdf Saefuddin, Asep and Setiabudi, Nur Andi and Fitrianto, Anwar (2012) On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data. World Applied Sciences Journal, 19 (2). pp. 205-210. ISSN 1818-4952; ESSN: 1991-6426 https://www.idosi.org/wasj/wasj19(2)2012.htm 10.5829/idosi.wasj.2012.19.02.528 |
| spellingShingle | Saefuddin, Asep Setiabudi, Nur Andi Fitrianto, Anwar On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data |
| title | On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data |
| title_full | On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data |
| title_fullStr | On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data |
| title_full_unstemmed | On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data |
| title_short | On comparison between logistic regression and geographically weighted logistic regression: with application to Indonesian poverty data |
| title_sort | on comparison between logistic regression and geographically weighted logistic regression with application to indonesian poverty data |
| url | http://psasir.upm.edu.my/id/eprint/44211/1/On%20comparison%20between%20logistic%20regression%20and%20geographically%20weighted%20logistic%20regression%20with%20application%20to%20Indonesian%20poverty%20data.pdf |
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