ツルタ　ヤスヒト   TSURUTA YASUHITO   鶴田　靖人    所属   明治大学  経営学部    職種   専任准教授
発行・発表の年月	2022/07
形態種別	学術雑誌
査読	査読あり
標題	Improving kernel-based nonparametric regression for circular–linear data
執筆形態	共著(筆頭者)
掲載誌名	Japanese Journal of Statistics and Data Science
掲載区分	国外
巻・号・頁	5(1),111-131頁
担当区分	責任著者
著者・共著者	Yasuhito Tsuruta,Masahiko Sagae
概要	We discuss kernel-based nonparametric regression where a predictor has support on a circle and a responder has support on a real line. Nonparametric regression is used in analyzing circular–linear data because of its flexibility. However, nonparametric regression is generally less accurate than an appropriate parametric regression for a population model. Considering that statisticians need more accurate nonparametric regression models, we investigate the performance of sine series local polynomial regression while selecting the most suitable kernel class. The asymptotic result shows that higher-order estimators reduce conditional bias; however, they do not improve conditional variance. We show that higher-order estimators improve the convergence rate of the weighted conditional mean integrated square error.
DOI	10.1007/s42081-022-00145-3
ISSN	/2520-8764