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Expectile Regression With Errors-in-Variables
IEEE Access, 2023 This paper studies the expectile regression with error-in-variables to reduce the data error and describe the overall data distribution. Specifically, the asymptotic normality of the proposed estimator is thoroughly investigated, and an IRWLS algorithm ...
Xiaoxia He, Xiaodan Zhou, Chunli Li
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Identification of the separately excited DC motor with extended instrumental variables
Вестник Самарского государственного технического университета. Серия: Технические науки, 2022 The problem of identifying the parameters of a DC motor is considered. The presence of measurement errors of currents and voltages leads to errors in both input and output signals. Existing methods for identifying the parameters of a DC motor do not take
Dmitry V. Ivanov +2 more
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Identification of linear dynamic systems of fractional order with errors in variables based on an augmented system of equations [PDF]
Vestnik Samarskogo Gosudarstvennogo Tehničeskogo Universiteta. Seriâ: Fiziko-Matematičeskie Nauki, 2021 Equations with derivatives and fractional order differences are widely used to describe various processes and phenomena. Currently, methods of identification of systems described by equations with fractional order differences are actively developing. The
Dmitriy V. Ivanov
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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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Regression Estimation with Errors in the Variables via the Laplace Transform
Axioms, 2023 This paper considers nonparametric regression estimation with errors in the variables. It is a standard assumption that the characteristic function of the covariate error does not vanish on the real line. This assumption is rather strong.
Huijun Guo, Qingqun Bai
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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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Prediction in polynomial errors-in-variables models
Modern Stochastics: Theory and Applications, 2020 A multivariate errors-in-variables (EIV) model with an intercept term, and a polynomial EIV model are considered. Focus is made on a structural homoskedastic case, where vectors of covariates are i.i.d. and measurement errors are i.i.d. as well.
Alexander Kukush, Ivan Senko
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IEEE Access, 2023 In this paper a Maximum likelihood estimation algorithm for Finite Impulse Response Errors-in-Variables systems is developed. We consider that the noise-free input signal is Gaussian-mixture distributed.
Angel L. Cedeno +4 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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Statistical inference for partially linear errors-in-variables panel data models with fixed effects
Systems Science & Control Engineering, 2021 In this paper, we consider the statistical inference for the partially linear panel data models with fixed effects. We focus on the case where some covariates are measured with additive errors. We propose a modified profile least squares estimator of the
Bangqiang He, Minxiu Yu, Jinming Zhou
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