Semiparametric analysis of isotonic errors-in-variables regression models with randomly right censored response

2013 ◽  
Vol 26 (3) ◽  
pp. 441-461 ◽  
Author(s):  
Zhimeng Sun ◽  
Zhongzhan Zhang ◽  
Jiang Du
Keyword(s):  
Regression Models ◽  
Errors In Variables ◽  
Semiparametric Analysis ◽  
Censored Response

Use of Errors-in-Variables Regression Models for Studies with K-X Ray Fluorescence Bone Lead Measurements

Epidemiology ◽  
2009 ◽  
Vol 20 ◽  
pp. S170-S171
Author(s):  
Hector Lamadrid-Figueroa ◽  
Martha M Tellez-Rojo ◽  
Gustavo Angeles ◽  
Mauricio Hernandez-Avila ◽  
Howard Hu
Keyword(s):  
Regression Models ◽  
Errors In Variables ◽  
X Ray ◽  
Bone Lead

scholarly journals MEASUREMENT ERROR AND DECONVOLUTION IN SPACES OF GENERALIZED FUNCTIONS

2014 ◽  
Vol 30 (6) ◽  
pp. 1207-1246 ◽  
Author(s):  
Victoria Zinde-Walsh
Keyword(s):  
Measurement Error ◽  
Regression Models ◽  
Fourier Transforms ◽  
Generalized Functions ◽  
Well Posedness ◽  
Errors In Variables ◽  
Absolutely Continuous ◽  
Regression Functions ◽  
Deconvolution Problem ◽  
Convolution Equations

This paper considers convolution equations that arise from problems such as measurement error and nonparametric regression with errors in variables with independence conditions. The equations are examined in spaces of generalized functions to account for possible singularities; this makes it possible to consider densities for arbitrary and not only absolutely continuous distributions, and to operate with Fourier transforms for polynomially growing regression functions. Results are derived for identification and well-posedness in the topology of generalized functions for the deconvolution problem and for some regression models. Conditions for consistency of plug-in estimation for these models are provided.

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