碩士 / 國立彰化師範大學 / 統計資訊研究所 / 101 / In statistical applications, logistic regression has been a popular method for analyzing binary data accompanied by explanatory variables. But when the two classifications are extremely imbalanced, the estimation of model parameters has been shown to be severely biased and hence the inferences of relative risks based on a selected model would be inaccurate. In this paper, we focus on assessing the risk variations of rare events based on logistic regression models. Instead of selecting a best model based on a particular variable selection criterion, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain new risk estimates. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. Our proposed local model averaging procedure accounts for both estimation uncertainty and selection uncertainty, which are generally not considered by other modeling procedures. Therefore the proposed method has superior performance in various situations. We present complete simulations to assess the robustness of our approach and a real data example for necrotizing enterocolitis (NEC) is also applied for illustration.
| Identifer | oai:union.ndltd.org:TW/101NCUE5506068 |
| Date | January 2013 |
| Creators | Meng-Fan Huang, 黃孟凡 |
| Contributors | Chun-Shu Chen, 陳春樹 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 40 |
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