Nonparametric autocovariance function estimation

被引：17
|
作者：
Hyndman, RJ
Wand, MP
机构：
[1] Monash Univ, Dept Math & Stat, Clayton, Vic 3168, Australia
[2] Univ New S Wales, AGSM, Sydney, NSW 2052, Australia
来源：
AUSTRALIAN JOURNAL OF STATISTICS | 1997年 / 39卷 / 03期
关键词：
bandwidth; correlated errors; kernel smoothing; local polynomial; nonparametric regression; non-stationary model; time series;
D O I：
10.1111/j.1467-842X.1997.tb00694.x
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：
Nonparametric estimators of autocovariance functions for non-stationary time series are developed. The estimators are based on straightforward nonparametric mean function estimation ideas and allow use of any linear smoother (e.g. smoothing spline, local polynomial). The paper studies the properties of the estimators, and illustrates their usefulness through application to some meteorological and seismic time series.
引用
收藏
页码：313 / 324
页数：12
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