Generalized confidence interval for the slope in linear measurement error model

2010 ◽  
Vol 80 (8) ◽  
pp. 927-936 ◽  
Author(s):  
Jia-Ren Tsai
Keyword(s):  
Measurement Error ◽  
Confidence Interval ◽  
Linear Measurement ◽  
Error Model ◽  
Measurement Error Model ◽  
Generalized Confidence Interval

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 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.

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