About: Errors-in-variables models

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In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses.

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rdfs:label	Fehler-in-den-Variablen-Modell (de) Errors-in-variables models (en)
rdfs:comment	In der Statistik sind Fehler-in-den-Variablen-Modelle, auch Messfehlermodelle genannt, Regressionsmodelle für Regression mit stochastischen Regressoren, in der entweder die Antwortvariable oder einige erklärende Variablen mit Fehlern gemessen werden. (de) In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. (en)
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dcterms:subject	Regression models Least squares
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Link from a Wikipage to another Wikipage	Scalar (mathematics) Reliability (statistics) Deconvolution Dummy variable (statistics) Nuisance parameter Regression dilution Function (mathematics) Generalized method of moments Consistent estimator Nonparametric statistics Regression model Berkson error model Simple linear regression Standard normal Statistical unit Statistics Deming regression Identifiability Kernel (statistics) Total least squares Regression models Data collection Data set Hadamard product (matrices) Linear model Cumulant Fourier transform Normal distribution Parameter Proxy (statistics) Characteristic function (probability theory) Jerry Hausman Latent variable Latent variable model Least squares Edgeworth series Mark Thoma Continuous and discrete variables Measurement errors Independence (probability theory) Ordinary least squares Maximum likelihood Importance sampling Type I and type II errors Heteroscedasticity Nonlinear modelling Orthogonal regression Independent variables Attenuation bias Identifiable Instrumental variables Dependent variables NLLS Nadaraya–Watson estimator
Link from a Wikipage to an external page	http://mathsdemo.cf.ac.uk/maths/resources/Gillard_Tech_Report.pdf https://archive.org/details/elementsofeconom0003kmen/page/346 https://books.google.com/books%3Fid=Bxq7AAAAIAAJ&pg=PA346 https://books.google.com/books%3Fid=UXucAQAAQBAJ&pg=PA300
sameAs	Errors-in-variables models Errors-in-variables models Errors-in-variables models Errors-in-variables models Errors-in-variables models
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title	Lecture on Econometrics (en)
has abstract	In der Statistik sind Fehler-in-den-Variablen-Modelle, auch Messfehlermodelle genannt, Regressionsmodelle für Regression mit stochastischen Regressoren, in der entweder die Antwortvariable oder einige erklärende Variablen mit Fehlern gemessen werden. (de) In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples. For simple linear regression the effect is an underestimate of the coefficient, known as the attenuation bias. In non-linear models the direction of the bias is likely to be more complicated. (en)

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