Results 221 to 230 of about 630,158 (264)

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, Ali H., Schubert, K. E.   +3 more
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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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A Note on an 'Errors in Variables' Model

Journal of the American Statistical Association, 1966
Abstract 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, 2004
Frequency 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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Errors in Variables and the Individual Structural Equation

International Economic Review, 1983
The 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

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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Hypotheses Testing for Error-in-Variables Models

Annals of the Institute of Statistical Mathematics, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gimenez, Patricia, Bolfarine, Heleno, Colosimo, Enrico A.   +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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Continuous-time errors-in-variables filtering

Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2003
We consider estimation problems for a continuous-time linear system with a state disturbance and additive errors on the input and the output. The problem formulation and the estimation principle are deterministic. The derived filter is identical to the stochastic Kalman filter.
Ivan Markovsky, Jan C. Willems, Bart De Moor   +2 more
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A Fast Algorithm for Errors-in-Variables Filtering

IEEE Transactions on Automatic Control, 2012
This note concerns the optimal estimation of the input and output sequences of linear time-invariant errors-in-variables (EIV) processes. An efficient recursive filtering algorithm is proposed. It is an innovation-based approach that relies on the triangular decomposition of block Toeplitz matrices.
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