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

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 ON THE UNIFORM CONVERGENCE OF DECONVOLUTION ESTIMATORS FROM REPEATED MEASUREMENTS

2021 ◽  
pp. 1-22
Author(s):  
Daisuke Kurisu ◽  
Taisuke Otsu
Keyword(s):  
Multivariate Analysis ◽  
Measurement Error ◽  
Uniform Convergence ◽  
Measurement Errors ◽  
Convergence Rates ◽  
Free Variable ◽  
Error Model ◽  
Repeated Measurements ◽  
Empirical Characteristic Function ◽  
Measurement Error Model

This paper studies the uniform convergence rates of Li and Vuong’s (1998, Journal of Multivariate Analysis 65, 139–165; hereafter LV) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015, Journal of Multivariate Analysis 140, 31–46) for the classical measurement error model, where repeated noisy measurements on the error-free variable of interest are available. In contrast to LV, our assumptions allow unbounded supports for the error-free variable and measurement errors. Compared to Bonhomme and Robin (2010, Review of Economic Studies 77, 491–533) specialized to the measurement error model, our assumptions do not require existence of the moment generating functions of the square and product of repeated measurements. Furthermore, by utilizing a maximal inequality for the multivariate normalized empirical characteristic function process, we derive uniform convergence rates that are faster than the ones derived in these papers under such weaker conditions.

scholarly journals Quasi Score is more Efficient than Corrected Score in a Polynomial Measurement Error Model

Metrika ◽  
2006 ◽  
Vol 65 (3) ◽  
pp. 275-295 ◽  
Author(s):  
Sergiy Shklyar ◽  
Hans Schneeweiss ◽  
Alexander Kukush
Keyword(s):  
Measurement Error ◽  
Error Model ◽  
Measurement Error Model ◽  
Corrected Score

scholarly journals Application of a Repeat-Measure Biomarker Measurement Error Model to 2 Validation Studies: Examination of the Effect of Within-Person Variation in Biomarker Measurements

2011 ◽  
Vol 173 (6) ◽  
pp. 683-694 ◽  
Author(s):  
Sarah Rosner Preis ◽  
Donna Spiegelman ◽  
Barbara Bojuan Zhao ◽  
Alanna Moshfegh ◽  
David J. Baer ◽  
...  
Keyword(s):  
Measurement Error ◽  
Validation Studies ◽  
Error Model ◽  
Measurement Error Model ◽  
Repeat Measure
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