Skip to search formSkip to main contentSkip to account menu

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… 
Wikipedia (opens in a new tab)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2008
2008
The paper addresses the discrete-time linear process identification problem assuming noisy input and output records available for… 
Highly Cited
2007
Highly Cited
2007
We study the performance of conditional asset pricing models and multifactor models in explaining the German cross-section of… 
Review
2007
Review
2007
This paper gives an introduction and overview to the often under‐used measurement error model. The purpose is to provide a simple… 
2004
2004
We describe the requirements for manufacturing and maintaining alignment of the 8.4 m off-axis segments of the Giant Magellan… 
2002
2002
The problem of identifying dynamic errors-in-variables models is of fundamental interest in many areas like process control… 
1999
1999
The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of… 
1999
1999
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables… 
1997
1997
This paper presents an identification method for errors-in-variables systems with input-output measurements affected by white and… 
1997
1997
We develop a method of estimating a change‐point of an otherwise smooth function in the case of indirect noisy observations. As…