Results 271 to 280 of about 2,136,192 (344)

Identifiability in dynamic errors-in-variables models

The 22nd IEEE Conference on Decision and Control, 1983
Abstract. This paper is concerned with the identifiability of scalar linear dynamic errors‐in‐variables systems. The analysis is based on second moments only. The set of feasible systems corresponding to given second moments of the observations is described and conditions for identifiability are derived for the case of rational transfer functions.
Anderson, Brian D.O., Deistler, Manfred
openaire   +3 more sources

Regression prediction method that is based on the partial errors-in-variables model

Communications in statistics. Simulation and computation, 2018
The observation errors of independent variables in model prediction are typically neglected in traditional regression models, which leads to the decreased prediction accuracy. In this paper, a new regression prediction method that is based on the partial
Leyang Wang, Jianqiang Sun
semanticscholar   +1 more source

Identification of nonlinear errors-in-variables models

Automatica, 2002
The publication deals with a generalization of a classical eigenvalue-decomposition method first developed for errors-in-variables linear system identification. An identification algorithm is presented for nonlinear, but linear in parameters errors-in-variables models using nonlinear polynomial eigenvalue-eigenvector decompositions.
Vajk, I., Hetthéssy, J.
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Estimation in a semiparametric partially linear errors-in-variables model with inverse Gaussian kernel

Communications in Statistics - Theory and Methods, 2019
This paper proposes an estimation procedure for the partial linear regression models, when the explanatory variable of the non parametric component is supported on , and the covariates in the parametric part are contaminated by the measurement errors ...
Juxia Xiao, Xu Li, Jianhong Shi
semanticscholar   +1 more source

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.
K. Wolter, W. Fuller
semanticscholar   +3 more sources

Effective estimation of nonlinear errors-in-variables models

Communications in statistics. Simulation and computation, 2023
Model variables are inevitably affected and interfered by measurement errors in statistical models inference. In this paper, we investigate the effective estimators of unknown operators in the nonlinear errors-in-variables models.
Zhensheng Huang, Shuyu Meng, Ziyi Ye
semanticscholar   +1 more source

The Degenerate Bounded Errors-in-Variables Model

SIAM Journal on Matrix Analysis and Applications, 2001
The paper is devoted to a special case of the error-in-variable problem. It is viewed as total least squares with bounds on the uncertainty in the coefficient matrix. The chosen approach advantage is given as a motivation for further considerations. Corresponding proofs and algorithm synthesis are presented.
Chandrasekaran, S., Gu, M., Sayed, A. H., Schubert, K. E.   +3 more
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Hypotheses Testing for Error-in-Variables Models

Annals of the Institute of Statistical Mathematics, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gimenez, Patricia, Bolfarine, Heleno, Colosimo, Enrico A.   +2 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
openaire  

Error-in-variables models in calibration

Metrologia, 2017
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 control of the calibration ...
I Lira, D Grientschnig
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