Forecasting with unequally spaced data by a functional principal component approach

被引：0
|
作者：
Ana M. Aguilera
Francisco A. Ocaña
Mariano J. Valderrama
机构：
[1] Universidad de Granada,Departamento de Estadística e Investigación Operativa Facultad de Farmacia
[2] Universidad de Granada,Dpto. de Estadística e I.O., Facultad de Ciencias
来源：
Test | 1999年 / 8卷
关键词：
Karhunen-Loève expansion; least-squares linear prediction; orthogonal projection; principal components; 60G25; 60G12; 65F15;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The Principal Component Regression model of multiple responses is extended to forccast 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.
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页码：233 / 253
页数：20
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