Journal
MATHEMATICS
Volume 11, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/math11030607
Keywords
autoregressive model; additive noise; Yule-Walker equations; bias-compensated least squares; Frisch scheme; total least squares; errors-in-variables; prediction error method; maximum likelihood
Categories
Ask authors/readers for more resources
This paper provides an overview of the main methods used to identify autoregressive models with additive noises. It classifies the identification methods and discusses the advantages and disadvantages of each group. The article presents simulation results of various methods and offers recommendations for selecting the best methods.
This paper presents an overview of the main methods used to identify autoregressive models with additive noises. The classification of identification methods is given. For each group of methods, advantages and disadvantages are indicated. The article presents the simulation results of a large number of the described methods and gives recommendations on choosing the best methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
