Derivation of a state-space model by functional data analysis
type: Item
instance of: scholarly article
title: Derivation of a state-space model by functional data analysis
zbMATH Open document ID: 1050.62101
DOI: 10.1007/BF03354615
author: Yanyan Li
published in: Computational Statistics
publication date: 2004-11-24T00:00:00Z
review text: A state-space model is proposed for prediction of continuous stochastic processes observed at discrete points. It is based on spline-interpolation of the original process approximated by Karhunen-Loève expansions and Kalman-Bucy filtering techniques. Results of simulations for narrow-band processes and applications to Madrid stock market data are presented.
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 2118519
zbMATH Keywords: spline interpolation
zbMATH Keywords: Kalman filter
zbMATH Keywords: Karhunen-Loève expansion
MaRDI profile type: Publication
cites work: Approximation of estimators in the PCA of a stochastic process using B-splines
cites work: Forecasting time series by functional PCA. Discussion of several weighted approaches
cites work: ARCH models and financial applications
cites work: Q3795136
cites work: Q4113353
cites work: Kalman filtering on approximate state-space models
description: scientific article; zbMATH DE number 2118519