Results 11 to 20 of about 8,978,880 (312)
Comment on Identification in the Linear Errors in Variables Model [PDF]
Econometrica, 1986 \textit{A. Kapteyn} and \textit{T. J. Wansbeek} [ibid. 51, 1847-1849 (1983; Zbl 0542.62056)] considered the following multiple linear regression model with errors in variables: \[ (1)\quad y_ j=\xi '\!_ j\beta +\epsilon_ j,\quad (2)\quad x_ j=\xi_ j+\nu_ j,\quad j=1,...,n, \] where \(\xi_ j\), \(x_ j\), \(\nu_ j\), and \(\beta\) are k-vectors, \(y_ j\),
Bekker, P.A.
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Two models for linear comparative calibration [PDF]
International Journal of Metrology and Quality Engineering, 2012 We consider the comparative calibration problem in the case when linear relationship is assumed between two considered measuring devices with possibly different units and precisions.
Wimmer G., Witkovský V.
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Beta regression model nonlinear in the parameters with additive measurement errors in variables.
PLoS ONE, 2021 We propose in this paper a general class of nonlinear beta regression models with measurement errors. The motivation for proposing this model arose from a real problem we shall discuss here. The application concerns a usual oil refinery process where the
Daniele de Brito Trindade +4 more
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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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Asymptotic normality of total least squares estimator in a multivariate errors-in-variables model
Modern Stochastics: Theory and Applications, 2016 We consider a multivariate functional measurement error model $AX\approx B$. The errors in $[A,B]$ are uncorrelated, row-wise independent, and have equal (unknown) variances.
Alexander Kukush +1 more
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Identifiability of logistic regression with homoscedastic error: Berkson model
Modern Stochastics: Theory and Applications, 2015 We consider the Berkson model of logistic regression with Gaussian and homoscedastic error in regressor. The measurement error variance can be either known or unknown. We deal with both functional and structural cases.
Sergiy Shklyar
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Solution for a time-series AR model based on robust TLS estimation
Geomatics, Natural Hazards & Risk, 2019 We discuss an algorithm for the autoregression (AR) model as a typical time-series model. By analyzing the structure of the AR model, we highlight the shortcomings of traditional algorithms for model parameter estimation and propose an approach to ...
Yeqing Tao, Qiaoning He, Yifei Yao
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CO-REGISTRATION OF 3D POINT CLOUDS BY USING AN ERRORS-IN-VARIABLES MODEL [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Co-registration of point clouds of partially scanned objects is the first step of the 3D modeling workflow. The aim of co-registration is to merge the overlapping point clouds by estimating the spatial transformation parameters. In the literature, one of
U. Aydar +3 more
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Fitting an Equation to Data Impartially
Mathematics, 2023 We consider the problem of fitting a relationship (e.g., a potential scientific law) to data involving multiple variables. Ordinary (least squares) regression is not suitable for this because the estimated relationship will differ according to which ...
Chris Tofallis
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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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