Results 311 to 320 of about 1,735,832 (325)

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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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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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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Errors-in-Variables in Joint Population Pharmacokinetic/Pharmacodynamic Modeling

Biometrics, 2001
Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response.
Bennett, James, Wakefield, Jon
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Identification of dynamic errors-in-variables models

Automatica, 1996
From the conclusion: ``The problem of identifying a causal linear dynamic system excited by a stationary zero-mean noise with unknown rational spectrum is considered for the case when the input-output measurements are corrupted by additive and uncorrelated noises of unknown rational spectra.'' The authors show that under mild conditions, the model is ...
Castaldi, P., Soverini, U.
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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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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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Pearson Type II Errors-in-Variables Models

1999
Abstract In this paper, invariant error-in-variables models (EIVM) are studied. We show that considering special invariance assumptions about the observable variables is equivalent to replace the usual normal EIVM by special Pearson type II EIVM.
Heleno Bolfarine, Loretta Gasco, Pilar Iglesias   +2 more
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Linear dynamic errors-in-variables models

Journal of Econometrics, 1989
M. Deistler, B.D.O. Anderson
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Linear errors-in-variables models

Economics Letters, 1987
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