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Empirical likelihood estimation for linear regression models with AR(p) error terms with numerical examples.
- Source :
- Journal of Applied Statistics; Jul2022, Vol. 49 Issue 9, p2271-2286, 16p, 5 Charts, 3 Graphs
- Publication Year :
- 2022
-
Abstract
- Linear regression models are useful statistical tools to analyze data sets in different fields. There are several methods to estimate the parameters of a linear regression model. These methods usually perform under normally distributed and uncorrelated errors. If error terms are correlated the Conditional Maximum Likelihood (CML) estimation method under normality assumption is often used to estimate the parameters of interest. The CML estimation method is required a distributional assumption on error terms. However, in practice, such distributional assumptions on error terms may not be plausible. In this paper, we propose to estimate the parameters of a linear regression model with autoregressive error term using Empirical Likelihood (EL) method, which is a distribution free estimation method. A small simulation study is provided to evaluate the performance of the proposed estimation method over the CML method. The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples. [ABSTRACT FROM AUTHOR]
- Subjects :
- REGRESSION analysis
CHRONIC myeloid leukemia
Subjects
Details
- Language :
- English
- ISSN :
- 02664763
- Volume :
- 49
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- Journal of Applied Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 157355176
- Full Text :
- https://doi.org/10.1080/02664763.2021.1899142