Nonparametric estimation for dependent data

被引:3
|
作者
Johannes, Jan [1 ]
Rao, Suhasini Subba [2 ]
机构
[1] Catholic Univ Louvain, Inst Stat, B-1348 Louvain, Belgium
[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
关键词
density estimation; nonparametric regression; 2-mixing; nonlinear processes; DENSITY-ESTIMATION; TIME-SERIES; ASYMPTOTIC NORMALITY; MIXING PROPERTIES; REGRESSION; CONVERGENCE; PROPERTY;
D O I
10.1080/10485252.2010.484491
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In this paper, we consider nonparametric estimation for dependent data, where the observations do not necessarily come from a linear process. We study density estimation and also discuss associated problems in nonparametric regression, using the 2-mixing dependence measure. We compare the results under the 2-mixing with those derived under the assumption that the process is linear.
引用
收藏
页码:661 / 681
页数:21
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