書誌事項
- タイトル別名
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- Practical Aspects of Bias Reducing Estimators in Nonparametric Regression
- ノンパラメトリック カイキ ニ オケル バイアス シュクショウ スイテイリョウ ノ ジッサイテキ ソクメン
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説明
We discuss anew the kernel method, which is representative of approaches to nonparametric scatter plot smoothing. This paper proposes a new estimator obtained by adding an adjustment term to an initial estimator, where the initial estimator is the well known local polynomial estimator. An appealing feature of the proposed estimator is that it reduces bias; the effect can be observed especially when the true regression function has large curvature. In this paper, we emphasize practical aspects of the use of our proposal, such as introducing a reliable bandwidth selection method and its evaluation, constructing a pointwise approximate confidence interval for the true regression function based on asymptotic normality of the estimator, and comparing our proposal with existing estimators by conducting a large size simulation study.
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), 131-155, 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
- 1390001204442951936
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- NII論文ID
- 10014067165
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- NII書誌ID
- AN00330942
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- ISSN
- 18838081
- 02850370
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- NDL書誌ID
- 7213050
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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
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- 抄録ライセンスフラグ
- 使用不可
