Forecasting with unequally spaced data by a functional principal component approach

被引：22

作者：

Aguilera, AM ^{[1
]}

Ocaña, FA

Valderrama, MJ

机构：

[1] Univ Granada, Fac Ciencias, Dpto Estadist & IO, E-18071 Granada, Spain

[2] Univ Granada, Fac Farm, Dept Estadist & Invest Operat, Granada, Spain

来源：

TEST | 1999年 / 8卷 / 01期

关键词：

Karhunen-Loeve expansion; least-squares linear prediction; orthogonal projection; principal components;

D O I：

10.1007/BF02595871

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：

The Principal Component Regression model of multiple responses is extended to forecast a continuous-time stochastic process. Orthogonal projection on a subspace of trigonometric functions is applied in order to estimate the principal components using discrete-time observations from a sample of regular curves. The forecasts provided by this approach are compared with classical principal component regression on simulated data.

引用

页码：233 / 253

页数：21

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