Results 31 to 40 of about 2,019,213 (203)
Some Recent Advances in Measurement Error Models and Methods [PDF]
, 2005 A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in asymptotically biased estimators.
Augustin, Thomas, Schneeweiß, Hans
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Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model
, 2011 This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject ...
Alexandre G. Patriota +14 more
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NeuroImage, 2020 Comparing electric field simulations from individualized head models against in-vivo intra-cranial recordings is considered the gold standard for direct validation of computational field modeling for transcranial brain stimulation and brain mapping ...
Oula Puonti +3 more
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Scaled weighted total least-squares adjustment for partial errors-in-variables model
Journal of Geodetic Science, 2016 Scaled total least-squares (STLS) unify LS, Data LS, and TLS with a different choice of scaled parameter. The function of the scaled parameter is to balance the effect of random error of coefficient matrix and observation vector for the estimate of ...
Zhao J.
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Statistical post-processing of hydrological forecasts using Bayesian model averaging [PDF]
, 2018 Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ...
Ayari, Mehrez El +2 more
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Journal of Inequalities and Applications, 2020 In this paper, we estimate the mean of the partially linear single-index errors-in-variables model with missing response variables. The linear covariate is measured with additive error, therefore missing is not random.
Xin Qi, ZhuoXi Yu
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Consistency of the total least squares estimator in the linear errors-in-variables regression
Modern Stochastics: Theory and Applications, 2018 This paper deals with a homoskedastic errors-in-variables linear regression model and properties of the total least squares (TLS) estimator. We partly revise the consistency results for the TLS estimator previously obtained by the author [18]. We present
Sergiy Shklyar
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Geomatics, Natural Hazards & Risk, 2020 A difficulty in variance component estimation (VCE) is that the estimates may become negative, which is not acceptable in practice. This article presents two new methods for non-negative VCE that utilize the expectation maximization algorithm for the ...
Leyang Wang, Qiwen Wu
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Journal of Inequalities and Applications, 2020 In this paper, we focus on the response mean of the partially linear varying-coefficient errors-in-variables model with missing response at random. A simulation study is conducted to compare jackknife empirical likelihood method with normal approximation
Yuye Zou, Chengxin Wu
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Least‐correlation estimates for errors‐in‐variables models [PDF]
International Journal of Adaptive Control and Signal Processing, 2005 AbstractThis paper introduces an estimator for errors‐in‐variables models in which all measurements are corrupted by noise. The necessary and sufficient condition minimizing a criterion, defined by squaring the empirical correlation of residuals, yields a new identification procedure that we call least‐correlation estimator.
Jun, Byung-Eul, Bernstein, Dennis S.
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