Results 11 to 20 of about 2,032,232 (300)
On The Errors-In-Variables Model With Singular Dispersion Matrices
Journal of Geodetic Science, 2014 While the Errors-In-Variables (EIV) Model has been treated as a special case of the nonlinear Gauss- Helmert Model (GHM) for more than a century, it was only in 1980 that Golub and Van Loan showed how the Total Least-Squares (TLS) solution can be ...
Schaffrin B., Snow K., Neitzel F.
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SPECIFICATION TESTING FOR ERRORS-IN-VARIABLES MODELS [PDF]
Econometric Theory, 2020 This paper considers specification testing for regression models with errors-in-variables and proposes a test statistic comparing the distance between the parametric and nonparametric fits based on deconvolution techniques. In contrast to the methods proposed by Hall and Ma (2007, Annals of Statistics, 35, 2620–2638) and Song (2008, Journal of ...
Otsu, Taisuke, Taylor, Luke Nicholas
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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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Errors-in-variables beta regression models [PDF]
Journal of Applied Statistics, 2014 Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0, 1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is ...
Carrasco, J. +2 more
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Error-in-variables modelling for operator learning.
Proposed for presentation at the MSML22 in ,, 2022 Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dependent variables, experimental and
Ravi G. Patel +3 more
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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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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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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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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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High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting
, 2022 Recent research has focused on $\ell_1$ penalized least squares (Lasso) estimators for high-dimensional linear regressions in which the number of covariates $p$ is considerably larger than the sample size $n$.
Han, Yuefeng, Tsay, Ruey S.
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