Results 21 to 30 of about 83,889,227 (308)
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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Identification of Fractional Models of an Induction Motor with Errors in Variables
Fractal and Fractional, 2023 The skin effect in modeling an induction motor can be described by fractional differential equations. The existing methods for identifying the parameters of an induction motor with a rotor skin effect suggest the presence of errors only in the output ...
Dmitriy Ivanov
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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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General Total Least Squares Theory for Geodetic Coordinate Transformations
Applied Sciences, 2020 Datum transformations are a fundamental issue in geodesy, Global Positioning System (GPS) science and technology, geographical information science (GIS), and other research fields.
Yuxin Qin +3 more
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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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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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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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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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A Novel Framework to Harmonise Satellite Data Series for Climate Applications
Remote Sensing, 2019 Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record ...
Ralf Giering +6 more
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Estimation in a linear errors-in-variables model under a mixture of classical and Berkson errors
Modern Stochastics: Theory and Applications, 2021 A linear structural regression model is studied, where the covariate is observed with a mixture of the classical and Berkson measurement errors. Both variances of the classical and Berkson errors are assumed known.
Mykyta Yakovliev, Alexander Kukush
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