Results 31 to 40 of about 2,308,308 (297)
Mathematics, 2020 In regression analysis, oftentimes a linear (or linearized) Gauss-Markov Model (GMM) is used to describe the relationship between certain unknown parameters and measurements taken to learn about them.
Burkhard Schaffrin
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EntropyDue to the high dimensional integration over latent variables, computing marginal likelihood and posterior distributions for the parameters of a general hierarchical model is a difficult task.
Subhash R. Lele +2 more
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Open Mathematics, 2020 In this article, an errors-in-variables regression model in which the errors are negatively superadditive dependent (NSD) random variables is studied. First, the Marcinkiewicz-type strong law of large numbers for NSD random variables is established. Then,
Zhang Yu +3 more
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Nonparametric Regression with Errors in Variables
The Annals of Statistics, 1993 The effect of errors in variables in nonparametric regression estimation is examined. To account for errors in covariates, deconvolution is involved in the construction of a new class of kernel estimators. It is shown that optimal local and global rates of convergence of these kernel estimators can be characterized by the tail behavior of the ...
Fan, Jianqing, Truong, Young K.
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Adaptive wavelet multivariate regression with errors in variables
, 2016 In the multidimensional setting, we consider the errors-in-variables model. We aim at estimating the unknown nonparametric multivariate regression function with errors in the covariates.
Chichignoud, Michaël +3 more
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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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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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The Case of the Homogeneous Errors-In-Variables Model
Journal of Geodetic Science, 2014 Recently, it has been claimed that the HomogeneousErrors-In-Variables (HEIV) Model, where the lefthandside (LHS) vector is allowed to be multiplied withan unknown scale factor, would represent a generalizationof the regular EIV-Model for which a number ...
Schaffrin B., Snow K.
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A Non-Iterative Approach to Direct Data-Driven Control Design of MIMO LTI Systems
IEEE Access, 2023 This paper proposes a non-iterative direct data-driven technique that deals with linear time-invariant (LTI) controller design by directly identifying the controller from input-output data without using plant identification.
Mohammad Abuabiah +3 more
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Least squares regression with errors in both variables: case studies
Química Nova, 2013 Analytical curves are normally obtained from discrete data by least squares regression. The least squares regression of data involving significant error in both x and y values should not be implemented by ordinary least squares (OLS).
Elcio Cruz de Oliveira +1 more
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