✦ LIBER ✦

A consistent nonparametric test for linearity of AR(p) models

✍ Scribed by Yanqin Fan; Qi Li

Book ID: 117332955
Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 160 KB
Volume: 55
Category: Article
ISSN: 0165-1765
DOI: 10.1016/s0165-1765(97)00054-2

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Towards a nonparametric test of linearit

Towards a nonparametric test of linearity for times series

✍ Ricardo Rios 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 906 KB

We study the intervals of linearity for the regression function of stationary and strongly mixing vectorial-valued random processes, using nonlinear integral plug-in estimators of functionals of the second derivative of the regression function. We give conditions in order to obtain CLT, possibly deg

Nonparametric conservative bands for the

Nonparametric conservative bands for the trend of Gaussian AR(p) models

✍ R. Fraiman; G. Pérez-Iribarren 📂 Article 📅 1996 🏛 CrossRef test prefix 🌐 English ⚖ 668 KB

A Test of Linearity for Functional Autor

A Test of Linearity for Functional Autoregressive Models

✍ Jean-Michel Poggi; Bruno Portier 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 680 KB

Testing the hypothesis of a general line

Testing the hypothesis of a general linear model using nonparametric regression estimation

✍ W. González-Manteiga; R. Cao 📂 Article 📅 1993 🏛 CrossRef test prefix 🌐 English ⚖ 933 KB

A Nonparametric Test of Conditional Auto

A Nonparametric Test of Conditional Autoregressive Heteroscedasticity for Threshold Autoregressive Models

✍ Min Chen and Gemai Chen 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 French ⚖ 806 KB

Estimation and test of linearity for a c

Estimation and test of linearity for a class of additive nonlinear models

✍ Nathalie Chèze-Payaud; Jean-Michel Poggi; Bruno Portier 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 619 KB

This paper deals with the estimation and the test for linearity of models belonging to a class of additive nonlinear ones. We prove the joint asymptotic normality for a kernel estimator and provide a test for linearity of each function defining the model.