Lack-of-fit testing in errors-in-variables regression model with validation data

2009 ◽  
Vol 79 (6) ◽  
pp. 765-773 ◽  
Author(s):  
Weixing Song
Keyword(s):  
Regression Model ◽  
Errors In Variables ◽  
Validation Data ◽  
Lack Of Fit ◽  
Fit Testing

Asymptotic for LS estimators in the EV regression model for dependent errors

Filomat ◽  
2017 ◽  
Vol 31 (15) ◽  
pp. 4845-4856
Author(s):  
Konrad Furmańczyk
Keyword(s):  
Regression Model ◽  
Functional Dependence ◽  
Dependent Data ◽  
Errors In Variables ◽  
Mixing Conditions ◽  
Asymptotic Results ◽  
Dependence Measure ◽  
Wide Range ◽  
Ev Regression Model ◽  
Linear And Nonlinear

We study consistency and asymptotic normality of LS estimators in the EV (errors in variables) regression model under weak dependent errors that involve a wide range of linear and nonlinear time series. In our investigations we use a functional dependence measure of Wu [16]. Our results without mixing conditions complete the known asymptotic results for independent and dependent data obtained by Miao et al. [7]-[10].

Examination of a Simple Errors-in-Variables Model: A Demonstration of Marginal Maximum Likelihood

2006 ◽  
Vol 31 (3) ◽  
pp. 311-325 ◽  
Author(s):  
Gregory Camilli
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Closed Form ◽  
Marginal Likelihood ◽  
Closed Form Expression ◽  
Errors In Variables ◽  
Marginal Maximum Likelihood ◽  
Form Expression ◽  
Marginal Maximum ◽  
Advanced Applications

A simple errors-in-variables regression model is given in this article for illustrating the method of marginal maximum likelihood (MML). Given suitable estimates of reliability, error variables, as nuisance variables, can be integrated out of likelihood equations. Given the closed form expression of the resulting marginal likelihood, the effects of error can be more clearly demonstrated. Derivations are given in detail to provide a detailed example of the marginalization strategy, and to prepare students for understanding more advanced applications of MML.

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