Parameter estimation of regression model with AR(p) error terms based on skew distributions with EM algorithm

In the linear regression model, the errors are usually assumed to be uncorrelated. However, in real-life data, this assumption is not often plausible. In this study, first, we will assume that the errors of the regression model have autoregressive structure. This type of regression models has been c...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2020-03, Vol.24 (5), p.3309-3330
Main Authors: Tuaç, Y., Güney, Y., Arslan, O.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Autoregressive models
Computational Intelligence
Confidence intervals
Control
Engineering
Errors
Estimators
Fisher information
Mathematical Logic and Foundations
Maximum likelihood estimates
Mechatronics
Methodologies and Application
Normal distribution
Parameter estimation
Random variables
Regression analysis
Regression models
Robotics
Skewed distributions
Skewness
Statistical analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the linear regression model, the errors are usually assumed to be uncorrelated. However, in real-life data, this assumption is not often plausible. In this study, first, we will assume that the errors of the regression model have autoregressive structure. This type of regression models has been considered before. However, in those papers under this assumption usually, the symmetric distributions are used as error distribution. The main contribution of this work is to use skew distributions instead of symmetric distributions as error distribution in regression models with autoregressive errors. We provide expectation maximization algorithm to compute the maximum likelihood estimates for the parameters. The performances of the proposed estimators are demonstrated with a simulation study and a real data example. We also provide the confidence intervals using the observed Fisher information matrix for the corresponding estimators.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-04089-x