Results 251 to 260 of about 2,287,103 (299)

Identification of nonlinear errors-in-variables models

Automatica, 2002
The publication deals with a generalization of a classical eigenvalue-decomposition method first developed for errors-in-variables linear system identification. An identification algorithm is presented for nonlinear, but linear in parameters errors-in-variables models using nonlinear polynomial eigenvalue-eigenvector decompositions.
Vajk, I., Hetthéssy, J.
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Linear errors-in-variables models

1984
In this paper we are concerned with the statistical analysis of systems, where both, inputs and outputs, are contaminated by errors. Models of this kind are called error-in-variables (EV) models. Let x t * . and y t * denote the “true” inputs and outputs respectively and let xt and yt denote the observed inputs and outputs, then the situation can be ...
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Error in Variables

2003
AbstractThis chapter analyses the standard regression model with errors in variables. It covers measurement error bias and unobserved heterogeneity bias, instrumental variable estimation with panel data. It presents estimates from Bover and Watson (2000) concerning economies of scale in a firm money demand equation.
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Identification in the Linear Errors in Variables Model

Econometrica, 1983
Consider the following multiple linear regression model with errors in variables: \(y_ j=\xi^ T\!_ j\beta +\epsilon_ j\), \(x_ j=\xi_ j+\nu_ j\), \(j=1,...,n\), where \(\xi_ j\), \(x_ j\), \(\nu_ j\), and \(\beta\) are k-vectors, \(y_ j\), \(\epsilon_ j\) are scalars. The \(\xi_ j\) are unobserved variables: instead the \(x_ j\) are observed.
Kapteyn, Arie, Wansbeek, Tom
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Errors in variables Models [PDF]

, 2014
the participation rate should increase with the player’s observed strength, and the ...
Philippe Février, Lionel Wilner
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Optimal errors-in-variables filtering

Automatica, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guidorzi, Roberto, Diversi, Roberto, Soverini, Umberto   +2 more
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Errors in Variables in Econometrics

1998
This article discusses the use of instrumental variables and grouping methods in the linear errors-in-variables or measurement error model. Comparisons are made between these methods, standard measurement error model methods with side conditions, least squares methods, and replicated models. It is demonstrated that there are close relationships between
Chi-Lun Cheng, John W. Ness
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Identifiability of errors in variables dynamic systems

Automatica, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Aguero, Juan C., Goodwin, Graham C.
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Error in Variable Conversion in Table

JAMA Surgery, 2023
Crisanto M, Torres, Elliott R, Haut, Joseph V, Sakran   +2 more
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Errors in Variables and Articles

Evaluation Review, 1982
Quasi-experimental evaluations of manpower training may be biased when the mean value of preprogrammed earnings differs for participants and nonparticipants or when the two groups differ in the degree to which they deviate from the long-run trend of earnings. Both sources of bias are addressed in Director (1979).
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