Results 181 to 190 of about 2,080 (215)

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 ...
Chandrasekaran, S., Golub, G. H., Gu, M., Sayed, Ali H.   +3 more
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Instrumental-Variable Estimation of an Error-Components Model

Econometrica, 1986
This paper presents a clear and concise discussion of (efficient) instrumental-variable estimators for an error-components-model. The paper starts with a common form of a model with a common form of an instrumental-variable estimator, for which four alternative special assumptions lead to four models, which are discussed in detail: 1.
Amemiya, Takeshi, MaCurdy, Thomas E
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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
Abstract 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 ...
I Lira, D Grientschnig
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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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A Maximum Likelihood Solution To The Errors In Variables And Errors In Equations Model

Multivariate Behavioral Research, 1977
This article provides a maximum likelihood estimation procedure for a linear model with errors in variables. Warren, et al, provide a least squares procedure for the same problem but which may be shown to be a special case of the more general approach suggested here.
D A, Rock, C E, Werts, R L, Linn, K G, Joreskog   +3 more
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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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Identification of Scalar Errors-in-Variables Models with Dynamics

IFAC Proceedings Volumes, 1985
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Estimation of errors-in-variables models

Proceedings of the 27th IEEE Conference on Decision and Control, 2003
The so-called errors-in-variables models pose serious problems to traditional statistical estimation because the Gaussian likelihood function, defined by the natural quadratic error measure, has a saddle point rather than a maximum. A discussion is presented of the estimation of such models, including the number of linear relations in them, based on ...
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MEASUREMENT ERROR MODELS FOR ORDINAL EXPOSURE VARIABLES MEASURED WITH ERROR

Statistics in Medicine, 1996
In dietary epidemiology, the key nutrient variables are often expressed in the quintile scale. The nutrients are often measured with error and it is of interest to consider estimates of relative risk for exposures in the quintile scale corrected for measurement error.
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