scholarly journals An Unbiased Two-Parameter Estimation with Prior Information in Linear Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jibo Wu
Keyword(s):  
Parameter Estimation ◽  
Linear Regression ◽  
Prior Information ◽  
Ordinary Least Squares ◽  
Parameter Estimator ◽  
Ordinary Least Squares Estimator ◽  
Theoretical Results ◽  
Two Parameter ◽  
Almost Unbiased ◽  
Better Than

We introduce an unbiased two-parameter estimator based on prior information and two-parameter estimator proposed by Özkale and Kaçıranlar, 2007. Then we discuss its properties and our results show that the new estimator is better than the two-parameter estimator, the ordinary least squares estimator, and explain the almost unbiased two-parameter estimator which is proposed by Wu and Yang, 2013. Finally, we give a simulation study to show the theoretical results.

scholarly journals Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Adewale F. Lukman ◽  
Kayode Ayinde ◽  
B. M. Golam Kibria ◽  
Segun L. Jegede
Keyword(s):  
Monte Carlo Simulation ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Ordinary Least Squares ◽  
M Estimator ◽  
Ordinary Least Squares Estimator ◽  
General Linear Regression Model ◽  
Two Parameter

The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two-parameter ridge-type modified M-estimator (RTMME) based on the M-estimator to deal with the combined problem resulting from multicollinearity and outliers. Through theoretical proofs, Monte Carlo simulation, and a numerical example, the proposed estimator outperforms the modified ridge-type estimator and some other considered existing estimators.

scholarly journals A Modified New Two-Parameter Estimator in a Linear Regression Model

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Adewale F. Lukman ◽  
Kayode Ayinde ◽  
Sek Siok Kun ◽  
Emmanuel T. Adewuyi
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Least Squares Estimator ◽  
Parameter Estimator ◽  
Ridge Estimator ◽  
Error Matrix ◽  
Liu Estimator ◽  
Special Cases ◽  
Ordinary Least Squares Estimator ◽  
Two Parameter

The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two-parameter estimator based on prior information for the vector of parameters is proposed to circumvent the problem of multicollinearity. This new estimator includes the special cases of the ordinary least squares estimator (OLSE), the ridge estimator (RRE), the Liu estimator (LE), the modified ridge estimator (MRE), and the modified Liu estimator (MLE). Furthermore, the superiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and Kaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a simulation study were conducted to illustrate the theoretical results.

Sign in / Sign up

Export Citation Format

Share Document