Robust Multinomial Logistic Regression
| Autor: | Yi-Ting Chen, 陳怡婷 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Classification is a method often used in biomedical researches. The most common situation is to divide patients into two categories that one is disease and the other is health. Therefore, the traditional logistic regression is the most widely used method in practice. In some situations, we can only observe possibly mislabeled response variables. It will get unreliable results if we still fit traditional logistic regression to the mislabeled data. The γ-logistic regression was proposed in Hung et al. (2018) to improve the estimation bias in the presence of mislabeling. There is a method of multinomial logistic regression because the response variables are not only divided into two categories. It may exist the problem of mislabeled variables as same as two categories. Therefore, we extend the γ-logistic regression to multinomial logistic regression. The merit of our method is to assign different weights for every sample, to regulate the bias of estimation without the need to model the mislabel probabilities. We propose the results of simulation and analyze the dataset of Thyroid Disease (New thyroid) by UCI to demonstrate the benefit our proposal. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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