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An Efficient Algorithm for a Bounded Errors-in-Variables Model
SIAM Journal on Matrix Analysis and Applications, 1999The 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. +3 more
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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, 1989An 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, 2017Abstract 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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Bayesian Analysis of Errors-in-Variables Regression Models
Biometrics, 1995Summary: 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
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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Identification of a class of dynamic errors‐in‐variables models
International Journal of Adaptive Control and Signal Processing, 1992AbstractDynamic errors‐in‐variables (EV) models are a new type of linear system models and have found extensive practical applications. One common and important concern with EV models is how to remove noise‐induced bias in parameter estimators. In this paper some significant extensions to the newly established bias‐eliminated least‐squares (BELS ...
Zheng, Wei-Xing, Feng, Chun-Bo
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Identification of Scalar Errors-in-Variables Models with Dynamics
IFAC Proceedings Volumes, 1985zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Semiparametric errors-in-variables models A Bayesian approach
Journal of Statistical Planning and Inference, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mallick, Bani K., Gelfand, Alan E.
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Prediction in Some Poisson Errors in Variables Models
Scandinavian Journal of Statistics, 1997Predictive distributions are developed and illustrated for prediction in some Poisson errors in variables models. Two different situations in which multiplicative treatment effects are appropriate are considered within the context of predicting counts of road accidents. Hierarchical prior structures are investigated, and numerical integration and Gibbs
Dunsmore, Ian R., Robson, David J.
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