A simulation study for a linear measurement error model when error variances vary between measurements

2005 ◽  
Vol 10 (1) ◽  
pp. 118-130
Author(s):  
Yasutaka Chiba ◽  
Yutaka Matsuyama ◽  
Tosiya Sato ◽  
Isao Yoshimura
Keyword(s):  
Measurement Error ◽  
Simulation Study ◽  
Linear Measurement ◽  
Error Model ◽  
Measurement Error Model

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 A Note on Testing Serial Correlation in Partially Linear Additive Measurement Error Models

2019 ◽  
Vol 11 (6) ◽  
pp. 65
Author(s):  
Jing Li ◽  
Xueyan Li
Keyword(s):  
Measurement Error ◽  
Simulation Study ◽  
Null Hypothesis ◽  
Serial Correlation ◽  
Error Model ◽  
Test Statistic ◽  
Chi Square ◽  
Measurement Error Model ◽  
Partially Linear ◽  
Testing Error

The paper considers the problem of testing error serial correlation of partially linear additive measurement error model. We propose a test statistic and show that it converges to the standard chi-square distribution under the null hypothesis. Finally, a simulation study is conducted to illustrate the performance of the test approach.

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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