Nonlinear Time Series Semiparametric and Nonparametric Methods

During the past two decades or so, there has been a lot of interest in both theoretical and empirical analysis of nonlinear time series data. Models and methods used have been based initially on parametric nonlinear or nonparametric time series models. Such parametric nonlinear models and relate...

Description complète

Enregistré dans:
Détails bibliographiques
Auteur principal: Gao, Jiti
Format: Livre
Langue:English
Publié: Chapman & Hall 2012
Sujets:
Accès en ligne:https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/30554
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
Thư viện lưu trữ: Thư viện Trường Đại học Đà Lạt
Description
Résumé:During the past two decades or so, there has been a lot of interest in both theoretical and empirical analysis of nonlinear time series data. Models and methods used have been based initially on parametric nonlinear or nonparametric time series models. Such parametric nonlinear models and related methods may be too restrictive in many cases. This leads to various nonparametric techniques being used to model nonlinear time series data. The main advantage of using nonparametric methods is that the data may be allowed to speak for themselves in the sense of determining the form of mathematical relationships between time series variables. In modelling nonlinear time series data one of the tasks is to study the structural relationship between the present observation and the history of the data set.