Results 271 to 280 of about 8,978,880 (312)

Identification of multivariable errors-in-variables models

1999 European Control Conference (ECC), 1999
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the simultaneous estimate of the model parameters and of the
Paolo Castaldi, Roberto Diversi, Roberto Guidorzi, Umberto Soverini   +3 more
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Estimation of the Quadratic Errors in Variables Model

Biometrika, 1982
The authors have constructed an estimator of the coefficient vector \(\beta\) in the quadratic functional model with errors \((e_ t,u_ t)\) that are independent normal random variables with zero mean and known covariance matrix. The asymptotic properties of the estimator have been studied.
Wolter, Kirk M., Fuller, Wayne A.
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Extending the Classical Normal Errors-in-Variables Model

Econometrica, 1980
IT IS WELL KNOWN that least-squares estimates of the coefficients of a regression equation are inconsistent if any of the regressors are measured with error. The nature of these inconsistencies has been examined by Aigner [1], Blomqvist [2], Chow [3], Levi [5], McCallum [6], and Wickens [10] for the case in which a single regressor is subject to ...
Garber, Steven, Klepper, Steven
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An Efficient Algorithm for a Bounded Errors-in-Variables Model

SIAM Journal on Matrix Analysis and Applications, 1999
The paper is devoted to the problem of parameter estimation in presence of bounded data uncertainties. The considered version of the problem incorporates a priori bounds on the size of the perturbations. It has a ``closed'' form solution that is obtained by solving an ``indefinite'' regularized least-square problem with a regression parameter that is ...
Shiv Chandrasekaran, Gene H. Golub, Ming Gu 0002, Ali H. Sayed   +3 more
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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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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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Robust Estimation in the Errors-in-Variables Model

Biometrika, 1989
An errors-in-variables model in linear regression is considered. The model describes data consisting of \((p+1)\)-tuples \(x_ 1,...,x_ n\) with \(x_ i=X_ i+\epsilon_ i\) and \(a_ 0'X_ i=b_ 0\), where \(X_ i\) and \(\epsilon_ i\) are nonobservable independent random vectors and \(a_ 0\) is a vector of length one. Orthogonal regression determines a and b
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Error-in-variables models in calibration

Metrologia, 2017
In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration ...
I Lira, D Grientschnig
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Bayesian Analysis of Errors-in-Variables Regression Models

Biometrics, 1995
Summary: Use of errors-in-variables models is appropriate in many practical experimental problems. However, inference based on such models is by no means straightforward. In previous analyses, simplifying assumptions have been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. We analyse errors-in-
Dellaportas, Petros, Stephens, David A.
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Errors-in-Variables Models in Parameter Bounding

1996
When all observed variables of a model are affected by noise, parameter estimation is known as the errors-in-variables problem. While parameter bounding methods and algorithms have been extensively developed in the case of exactly known regressor variables, little attention has been paid to the bounded errors-in-variables problem.
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