2値応答に対する縮小推定型ブースティング樹木の開発

DOI Web Site 被引用文献2件 参考文献9件

書誌事項

タイトル別名
  • Development of the Elasticnet Regulation Boosting Trees for Binary Response:
  • － 長野県 岡谷市における水道水満足度への応用 －
  • - Application to the Questionnarie Data about Satisfaction of City Water -

抄録

In a citizen opinion poll, the questionnaire list items are generally taken as binary list items. If the response is binary data, logistic regression is widely used for exploration of influential factors. Standard multiple logistic models are usually based on a linear combination of these exploratory variables. However, in a questionnaire study, the true model is rarely linear and the estimated models have poor predictive accuracy. To solve this problem, boosting methods can be used. In this paper, we propose a new boosting method for binary response cases, the logistic additive regression trees via penalized likelihood approach (EN-Bagging-MART), where an elastic net penalty is applied for the construction of regrarized logistic regression model. The performance of this method was evaluated by simulation. We have also illustrated the usefulness of the EN-Bagging-MART method in a study of satisfaction with city water.

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