A Review of the Research in Measurement Error Models

2013 ◽  
Vol 462-463 ◽  
pp. 68-71
Author(s):  
Yu Ying Jiang ◽  
Qiang Liu
Keyword(s):  
Systematic Review ◽  
Measurement Error ◽  
Additive Model ◽  
Error Model ◽  
Model Estimation ◽  
Errors In Variables ◽  
Measurement Error Model ◽  
Error Models ◽  
Measurement Error Models ◽  
General Measurement

The measurement error models or EV(errors-in-variables) Models have been widely promoted in the field of statistics since 1877. According to the characteristics of the errors in variables, EV models can mainly be divided into three types: the additive model, the general measurement error model and berkson measurement error model. The emphases of researches in the EV models mainly focus on the effects of model estimation, hypothesis testing and model selection. In this paper, we concentrate on the research by conducted a systematic review of EV Models, in order to make a reference for researchers and practitioners.

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 A Moment-Adjusted Imputation Method for Measurement Error Models

Biometrics ◽  
2011 ◽  
Vol 67 (4) ◽  
pp. 1461-1470 ◽  
Author(s):  
Laine Thomas ◽  
Leonard Stefanski ◽  
Marie Davidian
Keyword(s):  
Measurement Error ◽  
Imputation Method ◽  
Error Models ◽  
Measurement Error Models

Using measurement error models to account for georeferencing error in species distribution models

Ecography ◽  
2015 ◽  
Vol 39 (3) ◽  
pp. 305-316 ◽  
Author(s):  
Jorge Velásquez-Tibatá ◽  
Catherine H. Graham ◽  
Stephan B. Munch
Keyword(s):  
Measurement Error ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Error Models ◽  
Measurement Error Models
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