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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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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
Patel, Ravi G. +3 more
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Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories [PDF]
IEEE Journal of Biomedical and Health Informatics, 2021 Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain, has not been addressed.
Alizadeh Ashrafi, Reza +5 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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Consistency checks for particle filters [PDF]
, 2006 An "inconsistent" particle filter produces - in a statistical sense - larger estimation errors than predicted by the model on which the filter is based. Two test variables are introduced that allow the detection of inconsistent behavior.
Heijden, F. van der
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A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation [PDF]
, 2020 Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables.
Cheong, Taesu +3 more
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Testing the suitability of polynomial models in errors-in-variables problems [PDF]
, 2007 A low-degree polynomial model for a response curve is used commonly in practice. It generally incorporates a linear or quadratic function of the covariate.
Hall, Peter, Ma, Yanyuan
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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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Estimation of Nonlinear Errors-in-Variables Models
The Annals of Statistics, 1982 An estimation procedure is presented for the coefficients of the nonlinear functional relation, where observations are subject to measurement error. The distributional properties of the estimators are derived, and a consistent estimator of the covariance matrix is given.
Wolter, Kirk M., Fuller, Wayne A.
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