Results 251 to 260 of about 453,419 (275)
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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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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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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, 2021Jianping Xuan, Wenhao Du, Qi Xia
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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Effects of positional errors in model-assisted and model-based estimation of growing stock volume
Remote Sensing of Environment, 2016Svetlana Saarela +2 more
exaly
Accounting for model errors in iterative ensemble smoothers
Computational Geosciences, 2019Geir Evensen
exaly