| ファイル情報(添付) | |
| タイトル |
Data sharpening on unknown manifold
|
| 著者 |
工藤 雅紀
|
| 収録物名 |
Communications in statistics. Theory and methods
|
| 巻 | 46 |
| 号 | 23 |
| 開始ページ | 11721 |
| 終了ページ | 11744 |
| 収録物識別子 |
ISSN 03610926
|
| 内容記述 |
その他
This article is concerned with data sharpening (DS) technique in nonparametric regression under the setting where the multivariate predictor is embedded in an unknown low-dimensional manifold. Theoretical asymptotic bias is derived, which reveals that the proposed DS estimator has a reduced bias compared to the usual local linear estimator. The asymptotic normality of the DS estimator is also developed. It can be confirmed from simulation and applications to real data that the bias reduction for the DS estimator supported on unknown manifold is evident.
|
| 主題 | |
| 言語 |
英語
|
| 資源タイプ | 学術雑誌論文 |
| 出版者 |
Taylor & Francis
|
| 発行日 | 2017-08-24 |
| 権利情報 |
This is an Accepted Manuscript of an article published by Taylor & Francis in 'Communications in statistics. Theory and methods' on 2017, available online: http://www.tandfonline.com/doi/full/10.1080/03610926.2016.1277756.
The full-text file will be made open to the public on August 25, 2018 in accordance with publisher's 'Terms and Conditions for Self-Archiving'.
|
| 出版タイプ | Accepted Manuscript（出版雑誌の一論文として受付されたもの。内容とレイアウトは出版社の投稿様式に沿ったもの） |
| アクセス権 | オープンアクセス |
| 関連情報 |
[DOI] 10.1080/03610926.2016.1277756
|
