We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
SOME ONE AND TWO PARAMETER ESTIMATORS FOR THE MULTICOLLINEAR GAUSSIAN LINEAR REGRESSION MODEL: SIMULATIONS AND APPLICATIONS.
- Authors
Hoque, Md Ariful; Kibria, B. M. Golam
- Abstract
The ordinary least square estimator is inefficient when there exists multicollinearity among regressors in linear regression model. There are many methods available in literature to solve the multicollinearity problem. In this study, we consider some one and two parameter estimators for estimating the regression parameters. We theoretically compared the estimators in terms of smaller mean squared error (MSE) criteria. A Monte Carlo simulation study has been conducted to compare the performance of the estimators numerically. Finally, for illustration purposes, a real-life data is analyzed.
- Subjects
REGRESSION analysis; MONTE Carlo method; MULTICOLLINEARITY; LEAST squares; SIMULATION methods &; models
- Publication
Surveys in Mathematics & its Applications, 2023, Vol 18, p183
- ISSN
1843-7265
- Publication type
Article