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Errors-in-variables models

Known as: Errors-in-variables, Errors-in-variables model, Measurement error model 
In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent… 
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Related topics

Related topics

14 relations
DeconvolutionDummy variable (statistics)Fixed effects modelImportance sampling

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2008
2008
Efficient Estimation of Errors-in-Variables Models
The paper addresses the discrete-time linear process identification problem assuming noisy input and output records available for… 
Highly Cited
2007
Highly Cited
2007
Cross-Sectional Tests of Conditional Asset Pricing Models: Evidence from the German Stock Market
We study the performance of conditional asset pricing models and multifactor models in explaining the German cross-section of… 
Review
2007
Review
2007
An Overview of Normal Theory Structural Measurement Error Models
This paper gives an introduction and overview to the often under‐used measurement error model. The purpose is to provide a simple… 
2004
2004
Manufacture of 8.4-m off-axis segments: a 1/5-scale demonstration
We describe the requirements for manufacturing and maintaining alignment of the 8.4 m off-axis segments of the Giant Magellan… 
2002
2002
Identification of dynamic errors-in-variables models
The problem of identifying dynamic errors-in-variables models is of fundamental interest in many areas like process control… 
1999
1999
Bootstrapping Errors-in-Variables Models
The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of… 
1999
1999
Identification of multivariable errors-in-variables models
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables… 
1997
1997
A frequential approach for errors-in-variables models
This paper presents an identification method for errors-in-variables systems with input-output measurements affected by white and… 
1997
1997
Optimal Change‐point Estimation in Inverse Problems
We develop a method of estimating a change‐point of an otherwise smooth function in the case of indirect noisy observations. As… 
1994
1994
Consistent identification of stochastic linear systems with noisy input-output data
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