Results 11 to 20 of about 601,288 (278)
NONPARAMETRIC INSTRUMENTAL REGRESSION WITH ERRORS IN VARIABLES [PDF]
Econometric Theory, 2018 This paper considers nonparametric instrumental variable regression when the endogenous variable is contaminated with classical measurement error. Existing methods are inconsistent in the presence of measurement error. We propose a wavelet deconvolution estimator for the structural function that modifies the generalized Fourier coefficients of the ...
Adusumilli, Karun, Otsu, Taisuke
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Errors-in-variables models: a generalized functions approach [PDF]
, 2007 Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined.
Victoria Zinde-Walsh
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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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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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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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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
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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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