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   Articles with "errors variables" as a keyword



Deep Errors-in-Variables using a diffusion model

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recommendations! Published in 2025 at "Machine Learning"

DOI: 10.1007/s10994-025-06744-x

Abstract: Errors-in-Variables is the statistical concept used to explicitly model input variable errors caused, for example, by noise. While it has long been known in statistics that not accounting for such errors can produce a substantial… read more here.

Keywords: deep errors; prediction performance; diffusion model; errors variables ... See more keywords

ARMA model identification from noisy observations based on a two-step errors-in-variables approach

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recommendations! Published in 2017 at "IFAC-PapersOnLine"

DOI: 10.1016/j.ifacol.2017.08.1857

Abstract: Abstract This paper proposes a new method for identifying ARMA models in the presence of additive white noise. The method operates with two main steps. First, the noisy ARMA model is approximated by the sum… read more here.

Keywords: variables approach; errors variables; arma model; model ... See more keywords

On maximum likelihood identification of errors-in-variables models

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recommendations! Published in 2017 at "IFAC-PapersOnLine"

DOI: 10.1016/j.ifacol.2017.08.634

Abstract: Abstract In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function… read more here.

Keywords: errors variables; maximum likelihood; variables models; likelihood identification ... See more keywords
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Non-Asymptotic Confidence Regions for Errors-In-Variables Systems

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recommendations! Published in 2018 at "IFAC-PapersOnLine"

DOI: 10.1016/j.ifacol.2018.09.060

Abstract: Abstract This paper deals with constructing non-asymptotic confidence regions for Errors-In-Variables (EIV) systems when there is noise on both the input and the output signal. The Leave-out Sign-dominant Correlation Regions (LSCR) approach originally devised for… read more here.

Keywords: errors variables; confidence; regions errors; confidence regions ... See more keywords
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Statistical estimation for a partially linear single-index model with errors in all variables

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recommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2018.1425446

Abstract: ABSTRACT This article considers partially linear single-index models with errors in all variables. By using the Pseudo − θ method (Liang, Härdle, and Carroll 1999), local linear regression and simulation-extrapolation (SIMEX) technique (Cook and Stefanski… read more here.

Keywords: errors variables; linear single; model; single index ... See more keywords
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Estimation on semi-functional linear errors-in-variables models

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recommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2018.1494836

Abstract: Abstract Semi-functional linear regression models are important in practice. In this paper, their estimation is discussed when function-valued and real-valued random variables are all measured with additive error. By means of functional principal component analysis… read more here.

Keywords: semi functional; errors variables; functional linear; variables models ... See more keywords

Finite Impulse Response Errors-in-Variables System Identification Utilizing Approximated Likelihood and Gaussian Mixture Models

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recommendations! Published in 2023 at "IEEE Access"

DOI: 10.1109/access.2023.3255827

Abstract: 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. We propose an Expectation-Maximization-based algorithm to estimate the system… read more here.

Keywords: response errors; mixture; errors variables; impulse response ... See more keywords

Data-Driven Superstabilizing Control Under Quadratically-Bounded Errors-in-Variables Noise

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recommendations! Published in 2024 at "IEEE Control Systems Letters"

DOI: 10.1109/lcsys.2024.3410888

Abstract: The Errors-in-Variables model of system identification/control involves nontrivial input and measurement corruption of observed data, resulting in generically nonconvex optimization problems. This letter performs full-state-feedback stabilizing control of all discrete-time linear systems that are consistent… read more here.

Keywords: superstabilizing control; control; driven superstabilizing; errors variables ... See more keywords

Non-asymptotic Confidence Regions for the Transfer Functions of Errors-in-Variables Systems

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recommendations! Published in 2022 at "IEEE Transactions on Automatic Control"

DOI: 10.1109/tac.2021.3080504

Abstract: Finite-sample system identification (FSID) methods provide guaranteed confidence regions for the unknown model parameter of dynamical systems under mild statistical assumptions for a finite number of data points. In this article, two FSID methods, the… read more here.

Keywords: non asymptotic; confidence; errors variables; eiv systems ... See more keywords

Robust Global Identification of LPV Errors-in-Variables Systems With Incomplete Observations

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recommendations! Published in 2022 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"

DOI: 10.1109/tsmc.2021.3071137

Abstract: This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input… read more here.

Keywords: errors variables; identification; robust global; identification lpv ... See more keywords

Symmetrical Convergence Rates and Asymptotic Properties of Estimators in a Semi-Parametric Errors-in-Variables Model with Strong Mixing Errors and Missing Responses

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recommendations! Published in 2024 at "Symmetry"

DOI: 10.3390/sym16111544

Abstract: This paper considers a semi-parametric errors-in-variables (EV) model, ηi=xiβ+g(τi)+ϵi, ξi=xi+δi, 1⩽i⩽n. The properties of estimators are investigated under conditions of missing data and strong mixing errors. Three approaches are used to handle missing data: direct… read more here.

Keywords: parametric errors; properties estimators; errors variables; semi parametric ... See more keywords