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Regression quantiles with errors-in-variables [PDF]
Journal of Nonparametric Statistics, 2009 Abstract In a lot of situations, variables are measured with errors. While this problem has been previously studied in the context of kernel regression, no work has been done in quantile regression. To estimate this function, we use deconvolution kernel estimators.
Ioannides Dimitri, A. +1 more
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Errors in Variables in Panel Data [PDF]
Journal of Econometrics, 1986 Abstract Panel data based studies in econometrics use the analysis of covariance approach to control for various ‘individual effects’ by estimating coefficients from the ‘within’ dimension of the data. Often, however, the results are unsatisfactory, with ‘too low’ and insignificant coefficients.
Zvi Griliches, Jerry A. Hausman
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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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An Overview of Linear Structural Models in Errors in Variables Regression
Revstat Statistical Journal, 2010 This paper aims to overview the numerous approaches that have been developed to estimate the parameters of the linear structural model. The linear structural model is an example of an errors in variables model, or measurement error model that has wide ...
Jonathan Gillard
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Errors in Variables in Linear Systems [PDF]
Econometrica, 1987 This paper extends the simple errors-in-variables bound to the setting of systems of equations. Both diagonal and nondiagonal measurement error covariance matrices are considered. In the nondiagonal case, the analogue of the simple errors-in-variables interval of estimates is an ellipsoid with diagonal equal to the line segment connecting the direct ...
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Errors-in-Variables Models [PDF]
, 2000 Errors-in-variables (EIV) models axe regression models in which the regres-sors axe observed with errors. These models include the linear EIV models, the nonlinear EIV models, and the partially linear EIV models. Suppose that we want to investigate the relationship between the yield (Y) of corn and available nitrogen (X) in the soil.
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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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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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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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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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