ASYMPTOTICS FOR PENALIZED ADDITIVE B-SPLINE REGRESSION

Takuma Yoshida, Kanta Naito
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引用次数: 20

Abstract

This paper is concerned with asymptotic theory for penalized spline estimator in bivariate additive model. The focus of this paper is put upon the penalized spline estimator obtained by the backfitting algorithm. The convergence of the algorithm as well as the uniqueness of its solution are shown. The asymptotic bias and variance of penalized spline estimator are derived by an efficient use of the asymptotic results for the penalized spline estimator in marginal univariate model. Asymptotic normality of estimator is also developed, by which an approximate confidence interval can be obtained. Some numerical experiments confirming theoretical results are provided.
惩罚加性b样条回归的渐近性
研究了二元加性模型中惩罚样条估计量的渐近理论。本文重点研究了由反拟合算法得到的惩罚样条估计量。证明了算法的收敛性和解的唯一性。利用边际单变量模型中惩罚样条估计量的渐近结果,得到了惩罚样条估计量的渐近偏差和方差。给出了估计量的渐近正态性,利用渐近正态性可以得到一个近似置信区间。给出了一些数值实验,验证了理论结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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