FREQUENCY-DOMAIN CHARACTERISTICS OF LINEAR OPERATOR TO DECOMPOSE A TIME-SERIES INTO THE MULTI-COMPONENTS

被引：8

作者：

HIGUCHI, T ^{[1
]}

机构：

[1] INST STAT MATH,MINATO KU,TOKYO 106,JAPAN

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 1991年 / 43卷 / 03期

关键词：

TIME SERIES; BAYESIAN APPROACH; SIGNAL DECOMPOSITION; LINEAR FILTER; VARIABLE KERNEL; CURVE SMOOTHING; SMOOTHNESS PRIOR; SEASONAL COMPONENT MODEL; QUASI-SINUSOIDAL WAVE EXTRACTION;

D O I：

10.1007/BF00053367

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：

Frequency domain properties of the operators to decompose a time series into the multi-components along the Akaike's Bayesian model (Akaike (1980, Bayesian Statistics, 143-165, University Press, Valencia, Spain)) are shown. In that analysis a normal disturbance-linear-stochastic regression prior model is applied to the time series. A prior distribution, characterized by a small number of hyperparameters, is specified for model parameters. The posterior distribution is a linear function (filter) of observations. Here we use frequency domain analysis or filter characteristics of several prior models parametrically as a function of the hyperparameters.

引用

页码：469 / 492

页数：24

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