Confidence Intervals for the Slope in a Linear Errors-in-Variables Regression Model

1987 ◽  
pp. 85-109 ◽  
Author(s):  
Leon Jay Gleser
Keyword(s):  
Regression Model ◽  
Confidence Intervals ◽  
Errors In Variables

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.

Using Bootstrapped Confidence Intervals for Improved Inferences with Seemingly Unrelated Regression Equations

1996 ◽  
Vol 12 (3) ◽  
pp. 569-580 ◽  
Author(s):  
Paul Rilstone ◽  
Michael Veall
Keyword(s):  
Monte Carlo ◽  
Regression Model ◽  
Confidence Intervals ◽  
Seemingly Unrelated Regression ◽  
Regression Coefficients ◽  
Standard Errors ◽  
Regression Equations ◽  
Unrelated Regression ◽  
Seemingly Unrelated Regression Model ◽  
Downward Bias

The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can result in substantially better inferences when applied to t-ratios rather than to standard errors.

scholarly journals The Limiting Distribution of Least Squares in an Errors-in-Variables Regression Model

1987 ◽  
Vol 15 (1) ◽  
pp. 220-233 ◽  
Author(s):  
Leon Jay Gleser ◽  
Raymond J. Carroll ◽  
Paul P. Gallo
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Limiting Distribution ◽  
Errors In Variables
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