scholarly journals The Mixed Liu Estimator in Stochastic Restricted Linear Measurement Error Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jibo Wu
Keyword(s):  
Measurement Error ◽  
Mean Squared Error ◽  
Linear Measurement ◽  
Error Model ◽  
Measurement Error Model ◽  
Error Matrix ◽  
Liu Estimator ◽  
Squared Error ◽  
Mixed Estimator ◽  
Linear Restrictions

Ghapani and Babdi [1] proposed a mixed Liu estimator in linear measurement error model with stochastic linear restrictions. In this article, we propose an alternative mixed Liu estimator in the linear measurement error model with stochastic linear restrictions. The performance of the new mixed Liu estimator over the mixed estimator, Liu estimator, and mixed Liu estimator proposed by Ghapani and Babdi [1] are discussed in the sense of mean squared error matrix. Finally, a simulation study is given to show the performance of these estimators.

scholarly journals A Variance Shift Model for Detection of Outliers in the Linear Measurement Error Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Babak Babadi ◽  
Abdolrahman Rasekh ◽  
Ali Akbar Rasekhi ◽  
Karim Zare ◽  
Mohammad Reza Zadkarami
Keyword(s):  
Measurement Error ◽  
Score Test ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Linear Measurement ◽  
Error Model ◽  
Test Statistics ◽  
Measurement Error Model ◽  
Empirical Distributions ◽  
Shift Model

We present a variance shift model for a linear measurement error model using the corrected likelihood of Nakamura (1990). This model assumes that a single outlier arises from an observation with inflated variance. The corrected likelihood ratio and the score test statistics are proposed to determine whether theith observation has an inflated variance. A parametric bootstrap procedure is used to obtain empirical distributions of the test statistics and a simulation study has been used to show the performance of proposed tests. Finally, a real data example is given for illustration.

scholarly journals On the Stochastic Restrictedr-kClass Estimator and Stochastic Restrictedr-dClass Estimator in Linear Regression Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jibo Wu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
Multiple Linear Regression Model ◽  
Error Matrix ◽  
Squared Error ◽  
The Mean ◽  
Linear Restrictions ◽  
Theoretical Results

The stochastic restrictedr-kclass estimator and stochastic restrictedr-dclass estimator are proposed for the vector of parameters in a multiple linear regression model with stochastic linear restrictions. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a numerical example is given to show some of the theoretical results.

scholarly journals Modifying Two-Parameter Ridge Liu Estimator Based on Ridge Estimation

2019 ◽  
pp. 881-890
Author(s):  
Tarek Mahmoud Omara
Keyword(s):  
Simulation Study ◽  
Mean Squared Error ◽  
Error Matrix ◽  
Liu Estimator ◽  
Mean Squared Error Matrix ◽  
Ridge Estimation ◽  
Squared Error ◽  
The Mean ◽  
Two Parameter

In this paper, we introduce the new biased estimator to deal with the problem of multicollinearity. This estimator is considered a modification of Two-Parameter Ridge-Liu estimator based on ridge estimation. Furthermore, the superiority of the new estimator than Ridge, Liu and Two-Parameter Ridge-Liu estimator were discussed. We used the mean squared error matrix (MSEM) criterion to verify the superiority of the new estimate.  In addition to, we illustrated the performance of the new estimator at several factors through the simulation study.

Replicated measurement error model under exact linear restrictions

2012 ◽  
Vol 55 (2) ◽  
pp. 253-274 ◽  
Author(s):  
Sukhbir Singh ◽  
Kanchan Jain ◽  
Suresh Sharma
Keyword(s):  
Measurement Error ◽  
Error Model ◽  
Measurement Error Model ◽  
Replicated Measurement ◽  
Linear Restrictions
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