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Errors-in-Variables Models in Parameter Bounding
1996When 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, 2001Pharmacokinetic (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, 1996From 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, 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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Bayesian inference for the Errors-In-Variables model
Studia Geophysica et Geodaetica, 2016X. Fang +4 more
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TWO-STEP GMM ESTIMATION OF THE ERRORS-IN-VARIABLES MODEL USING HIGH-ORDER MOMENTS
Econometric Theory, 2002Timothy Erickson, Toni M. Whited
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Pearson Type II Errors-in-Variables Models
1999Abstract 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 +2 more
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Minimum Distance Estimation of the Errors-In-Variables Model Using Linear Cumulant Equations
, 2014Timothy Erickson +2 more
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
exaly
Instrumental variable estimator for the nonlinear errors-in-variables model
, 1985Yasuo Amemiya
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