A state space model for non-stationary functional data

被引：0

作者：

Ortega-Moreno, M ^{[1
]}

Valderrama, MJ ^{[1
]}

Ruiz-Molina, JC ^{[1
]}

机构：

[1] Univ Heulva, Dept Gen Econ & Stat, Fac Econ Sci, Huelva 21071, Spain

来源：

COMPSTAT 2002: PROCEEDINGS IN COMPUTATIONAL STATISTICS | 2002年

关键词：

Karhunen-Loeve expansion; Kalman-Bucy filter; B-splines; Brownian motion;

D O I：

暂无

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：

A time dependent state space model with minimal dimension is introduced in this paper by approximating the stochastic process of continuous time nature by means of spline interpolation of its sample paths and then by differentiating its Karhunen-Loeve expansion. A comparative study of forecasting, using the Kalman-Bucy filter, with simulated data is presented from a well known non-stationary process, the Brownian motion, discussing its advantages.

引用

页码：135 / 140

页数：6

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