scholarly journals Using stochastic prior information in consistent estimation of regression coefficients in replicated measurement error model

2012 ◽  
Vol 111 ◽  
pp. 198-212 ◽  
Author(s):  
Sukhbir Singh ◽  
Kanchan Jain ◽  
Suresh Sharma
Keyword(s):  
Measurement Error ◽  
Prior Information ◽  
Error Model ◽  
Regression Coefficients ◽  
Consistent Estimation ◽  
Measurement Error Model ◽  
Replicated Measurement

scholarly journals Consistent estimation in an implicit quadratic measurement error model

2004 ◽  
Vol 47 (1) ◽  
pp. 123-147 ◽  
Author(s):  
Alexander Kukush ◽  
Ivan Markovsky ◽  
Sabine Van Huffel
Keyword(s):  
Measurement Error ◽  
Error Model ◽  
Consistent Estimation ◽  
Measurement Error Model

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 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.

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