Results 221 to 230 of about 606,553 (265)

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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Identifiability of errors in variables dynamic systems

Automatica, 2006
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Aguero, Juan C., Goodwin, Graham C.
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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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The Degenerate Bounded Errors-in-Variables Model

SIAM Journal on Matrix Analysis and Applications, 2001
The paper is devoted to a special case of the error-in-variable problem. It is viewed as total least squares with bounds on the uncertainty in the coefficient matrix. The chosen approach advantage is given as a motivation for further considerations. Corresponding proofs and algorithm synthesis are presented.
Chandrasekaran, S., Gu, M., Sayed, A. H., Schubert, K. E.   +3 more
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Procrustes Errors-In-Variables Solutions

2019
As shown in the previous chapters, given a matrix \(\mathbf {A}\) (origin) and a matrix \(\mathbf {B}\) (destination), containing the coordinates of p-points in \(\mathbb {R}^k\), classical least squares (LS) Procrustes solutions find the transformation parameters between the two point sets assuming that all random errors are confined to the ...
Fabio Crosilla, Alberto Beinat, Andrea Fusiello, Eleonora Maset, Domenico Visintini   +4 more
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Algorithms for optimal errors-in-variables filtering

Systems & Control Letters, 2003
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Diversi, Roberto, Guidorzi, Roberto, Soverini, Umberto   +2 more
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Bootstrapping Errors-in-Variables Models

2000
The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of an estimate by using only the available data. Performance assessment through bootstrap provides the same or better accuracy than the traditional error propagation approach, most often without requiring complex analytical ...
Bogdan Matei, Peter Meer
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Hypotheses Testing for Error-in-Variables Models

Annals of the Institute of Statistical Mathematics, 2000
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Gimenez, Patricia, Bolfarine, Heleno, Colosimo, Enrico A.   +2 more
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Errors‐in‐variables jump regression using local clustering

Statistics in Medicine, 2019
Errors‐in‐variables (EIV) regression is widely used in econometric models. The statistical analysis becomes challenging when the regression function is discontinuous and the distribution of measurement error is unknown. In the literature, most existing jump regression methods either assume that there is no measurement error involved or require that ...
Yicheng Kang, Xiaodong Gong, Jiti Gao, Peihua Qiu   +3 more
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Error in Variables Parameter Estimation

Journal of Environmental Engineering, 1989
The application of the errors in variables method (EVM) to environmental engineering practice using, as an example, the determination of kinetic parameters from a laboratory biodegradability study is presented. Model parameters must often be estimated from field or laboratory observations to describe biological treatability studies, adsorption, and ...
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