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Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model
Communications in Statistics - Simulation and Computation, 2009 Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated,
Cibele M. Russo +2 more
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Metrika, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chi-Lun Cheng, Alexander Kukush
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Communications in Statistics - Theory and Methods, 1977 Two multivariate ‘errors in variables’ regression models are considered which generalize a model proposed by Gleser and Watson by allowing the errors of measurement e and f in the independent and dependent vector variables X and Y, respectively, to have common unknown covariance matrix Σ, rather than Σ = σ2I, as assumed by Gleser and Watson.
Anil K. Bhargava
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On Consistent Estimators in Linear and Bilinear Multivariate Errors-In-Variables Models
, 2002 We consider three multivariate regression models related to the TLS problem. The errors are allowed to have unequal variances.
Alexander Kukush +2 more
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Identification of multivariable errors-in-variables models
1999 European Control Conference (ECC), 1999The 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
P. Castaldi +3 more
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Frisch scheme–based identification of multivariable errors–in–variables models
IFAC Proceedings Volumes, 2009Abstract This paper describes an identification procedure for minimally parametrized multivariable models in the Errors–in–Variables (EIV) context of the Frisch scheme that considers additive white observation noise on the process inputs and outputs.
DIVERSI, ROBERTO, GUIDORZI, ROBERTO
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Partial Multivariate Errors-in-Variables Model and Its Application in Settlement Monitoring
Numerical Analysis and ApplicationsQisheng Wang, Frank B. Hu
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S37.3: Multivariate Calibration and Estimation for Linear Models with Errors‐In‐Variables
Biometrical Journal, 2004 Bernd‐Wolfgang Igl, Lutz Duembgen
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Identification of multivariable errors in variable models with dynamics
IEEE Transactions on Automatic Control, 1986This paper extends to the multivariable case the results presented for scalar systems by the second author [Automatica 21, 709-716 (1985)]. The problem considered is that of identifying a causal, linear, dynamic multivariable system from measurements of the input and output signals corrupted by noises of unknown spectra. To solve this so-called errors-
Green, Michael, Anderson, Brian D. O.
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Subspace-based methods for the identification of multivariable dynamic errors-in-variables models
Proceedings of 35th IEEE Conference on Decision and Control, 2002This paper analyses a multivariable errors-in-variables problem under rather general noise assumptions. Apart from the fact that both the measured input and output are corrupted by additive white noise, the output is also contaminated by a term which is caused by a white input process noise.
C.T. Chou, M.H. Verhaegen
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