Back to Search
Start Over
Dynamic functional data analysis with non-parametric state space models.
- Source :
-
Journal of Applied Statistics . Jan2014, Vol. 41 Issue 1, p142-163. 22p. - Publication Year :
- 2014
-
Abstract
- In this article, we introduce a new method for modelling curves with dynamic structures, using a non-parametric approach formulated as a state space model. The non-parametric approach is based on the use of penalised splines, represented as a dynamic mixed model. This formulation can capture the dynamic evolution of curves using a limited number of latent factors, allowing an accurate fit with a small number of parameters. We also present a new method to determine the optimal smoothing parameter through an adaptive procedure, using a formulation analogous to a model of stochastic volatility (SV). The non-parametric state space model allows unifying different methods applied to data with a functional structure in finance. We present the advantages and limitations of this method through simulation studies and also by comparing its predictive performance with other parametric and non-parametric methods used in financial applications using data on the term structure of interest rates. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 02664763
- Volume :
- 41
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Applied Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 92562563
- Full Text :
- https://doi.org/10.1080/02664763.2013.838663