ID | 39244 |
ファイル | |
言語 |
英語
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著者 |
工藤 雅紀
島根大学大学院総合理工学研究科
内藤 貫太
島根大学大学院総合理工学研究科
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内容記述(抄録等) | 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.
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主題 | Bias reduction
Data sharpening
Manifold
Non parametric regression
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掲載誌名 |
Communications in statistics. Theory and methods
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巻 | 46
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号 | 23
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開始ページ | 11721
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終了ページ | 11744
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ISSN | 03610926
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発行日 | 2017-08-24
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DOI | |
DOI公開日 | 2017-01-13
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出版者 | Taylor & Francis
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資料タイプ |
学術雑誌論文
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ファイル形式 |
PDF
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権利関係 | 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'.
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著者版/出版社版 |
著者版
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業績ID | e31576
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部局 |
(旧組織)大学院総合理工学研究科
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