碩士 / 淡江大學 / 數學學系數學與數據科學碩士班 / 106 / Threshold model is a model based on logistic model but with a threshold point that makes the probability discontinuous. Most of traditional methods use likelihood based approach to estimate the threshold. In this report, we use SVM (support vector machine) to help us to identify the change point in logistic regression. SVM is a popular classifier in machine learning. It constructs a hyperplane between two perfectly separated classes. If there are any change point in the model, it must have something to do with the hyperplane. However, SVM only do well when the probability discontinuous point is around p=0.5. When it is not around p=0.5, we generate new observation based on the original observation, so that the probability discontinuous point can be shifted to be around p=0.5. And then we use the hyperplane which was determined by the quality regrading p-value as the threshold function
We compare the ability of the methods on different simulated situation. According to this report, SVM is effective to find the threshold function in some particular limited situations. However, whether it could be adapted for all situations or not, that will await for further researches and studies.
| Identifer | oai:union.ndltd.org:TW/106TKU05477006 |
| Date | January 2018 |
| Creators | Wei-che Cheng, 鄭為澤 |
| Contributors | Yih-huei Huang, 黃逸輝 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 42 |
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