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Guest editorial: Total least squares and errors-in-variables modeling
, 2007 Van Huffel, S. +3 more
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, 2011 1 Title: Errors in variables Author: Katarína Mordinová Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Zdeněk Hlávka, Ph.D. Supervisor's e-mail address: Zdenek.Hlavka@mff.cuni.cz Abstract: The topic of the diploma thesis is Errors in variables.
Mordinová, Katarína
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Bootstrapping Errors-in-Variables Models
2000The 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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A Note on an 'Errors in Variables' Model
Journal of the American Statistical Association, 1966Abstract We consider an errors in variables model in which the ‘true’ part of the determining variable is generated by a simple forecasting mechanism. It is shown that the Least Squares errors in variables bias can be interpreted in terms of the parameters of the forecasting mechanism; and that the ‘standard’ result for this bias may no longer hold in ...
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The Variance of Nonparametric Errors- in-Variables Estimates
IEEE Transactions on Instrumentation and Measurement, 2004Frequency response functions (FRFs) measured by taking the ratio of the output to the input Fourier coefficients of the steady-state response of the system to a periodic excitation are considered. Under assumptions of additive Gaussian noise on both the inputs and outputs, the variance of such measurements is infinite.
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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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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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Errors in Variables and the Individual Structural Equation
International Economic Review, 1983The main intention of this paper is a practical (from the applied point of view) and simple method of dealing with the problem of measurement errors in simultaneous equation models. As a result, the paper provides the conditions under which certain simultaneous equations models can be identified and estimated on a recursive equation-by-equation basis ...
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Linear errors-in-variables models
1984In 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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Hypotheses Testing for Error-in-Variables Models
Annals of the Institute of Statistical Mathematics, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gimenez, Patricia +2 more
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