Results 261 to 270 of about 117,319,651 (289)
Some of the next articles are maybe not open access.
Metrika, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cheng, Chi-Lun, Kukush, Alexander
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cheng, Chi-Lun, Kukush, Alexander
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Ukrainian Mathematical Journal, 2007
We consider a linear multivariate errors-in-variables model AX ≈ B, where the matrices A and B are observed with errors and the matrix parameter X is to be estimated. In the case of lack of information about the error covariance structure, we propose an estimator that converges in probability to X as the number of rows in A tends to infinity ...
O. H. Kukush, M. Ya. Polekha
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We consider a linear multivariate errors-in-variables model AX ≈ B, where the matrices A and B are observed with errors and the matrix parameter X is to be estimated. In the case of lack of information about the error covariance structure, we propose an estimator that converges in probability to X as the number of rows in A tends to infinity ...
O. H. Kukush, M. Ya. Polekha
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On Consistent Estimators in Linear and Bilinear Multivariate Errors-In-Variables Models
2002We 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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Advanced Materials Research, 2012
The multivariate linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. As well known, when sample data is contaminated by large numbers of awkwardly placed outliers, the least squares estimator isn’t robust.
Hui Rong Cao, Fu Chang Wang
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The multivariate linear errors-in-variables (EIV) model is frequently used in computer vision for model fitting tasks. As well known, when sample data is contaminated by large numbers of awkwardly placed outliers, the least squares estimator isn’t robust.
Hui Rong Cao, Fu Chang Wang
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Metrika, 2004
For a multivariate measurement error model, the authors consider the elementwise weighted total least squares (TLS) estimator. This problem covers the whole class of problems in which the errors in each element are proportional to its size and is therefore an important extension of the class of TLS problems studied so far.
Kukush, Alexander, Van Huffel, Sabine
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For a multivariate measurement error model, the authors consider the elementwise weighted total least squares (TLS) estimator. This problem covers the whole class of problems in which the errors in each element are proportional to its size and is therefore an important extension of the class of TLS problems studied so far.
Kukush, Alexander, Van Huffel, Sabine
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A googness of-fit-test for a multivariate errors-in-variables model
2009A multivariate errors-in-variables model AX ??? B is considered, where the data matrices A and B are observed with errors, and a matrix parameter X is to be estimated. A goodness-of-???t test which is based on the moment estimator is constructed. The proposed test is asymptotically chi-squared under null hypothesis. The power of the test is discussed.
Kukush, A., Polekha, M.
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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.
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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.
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2010
Errors–in–Variables (EIV) models, i.e. models whose stochastic environment considers measurement errors on both inputs and outputs are intrinsically more realistic than representations assuming an exact knowledge of the input but are also more difficult to estimate.
DIVERSI, ROBERTO, GUIDORZI, ROBERTO
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Errors–in–Variables (EIV) models, i.e. models whose stochastic environment considers measurement errors on both inputs and outputs are intrinsically more realistic than representations assuming an exact knowledge of the input but are also more difficult to estimate.
DIVERSI, ROBERTO, GUIDORZI, ROBERTO
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Freeform surface topography model for ultraprecision turning under the influence of various errors
Journal of Manufacturing Processes, 2021Tielin Shi, Qi Xia, Jianping Xuan
exaly
S37.3: Multivariate Calibration and Estimation for Linear Models with Errors‐In‐Variables
Biometrical Journal, 2004Bernd‐Wolfgang Igl, Lutz Duembgen
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