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  • 标题:Asymptotics for Penalized Additive B-spline Regression
  • 本地全文:下载
  • 作者:Takuma Yoshida ; Kanta Naito
  • 期刊名称:JOURNAL OF THE JAPAN STATISTICAL SOCIETY
  • 印刷版ISSN:1882-2754
  • 电子版ISSN:1348-6365
  • 出版年度:2012
  • 卷号:42
  • 期号:1
  • 页码:81-107
  • DOI:10.14490/jjss.42.81
  • 出版社:JAPAN STATISTICAL SOCIETY
  • 摘要:This paper is concerned with the asymptotic theory for penalized spline estimators in additive models. The focus of this paper is on the penalized spline estimators obtained by the backfitting algorithm. The convergence of the algorithm as well as the uniqueness of its solution are shown. Asymptotic equivalence between the penalized spline estimators by the backfitting algorithm and the convenient estimators proposed by Marx and Eilers (1998) is addressed. Asymptotic normality of the estimators is also developed, by which an approximate confidence interval can be obtained. Some numerical experiments confirming theoretical results are provided.
  • 关键词:Additive model;asymptotic normality;backfitting algorithm;B-spline;penalized spline
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