書誌事項
- タイトル別名
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- Recursive Partitioning Linear Model Using Interaction Effect Criterion
- コウゴ サヨウ キジュン ニ ヨル サイキ ブンカツ センケイ モデル
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説明
In this paper, we propose a method to construct tree regression models including linear regression terms. Ordinary tree regression models reliably detect high order interactions compared with multiple regression models, but they tend to make large, deep trees because they explain all covariate effects using only stratification of samples. In particular, if there are many effects common to all samples or some sample group, they estimate a tree with many almost identical subtrees. In order to avoid this redundancy, we propose a tree regression model that explains main effects by linear regression terms in each node, and heterogeneity of the effects by stratification. When we take this strategy, there may be a huge number of candidate models; hence model selection is one of the main tasks. Thus we propose an algorithm to estimate the models using a criterion based on the MDL principle. This criterion can be interpreted as a criterion to select split variables which have the maximum interaction effects. Finally, we demonstrate the efficiency of our method using numerical examples as well as real data on the amount of time caregivers provided individually to elderly persons.
収録刊行物
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- 応用統計学
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応用統計学 33 (2), 111-130, 2004
応用統計学会
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詳細情報 詳細情報について
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- CRID
- 1390001204442953088
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- NII論文ID
- 10014067146
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- NII書誌ID
- AN00330942
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- ISSN
- 18838081
- 02850370
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- NDL書誌ID
- 7213034
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可
