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An intelligent hybrid deep learning-machine learning model for monthly groundwater level prediction. [PDF]

Sci Rep
Banadkooki FB, Ghanbari-Adivi E, Sayyahi F, Ehteram M.   +3 more
europepmc   +1 more source

A predictive model for cognitive decline using social determinants of health. [PDF]

JAR Life
He Y, Leng Y, Vranceanu AM, Ritchie CS, Blacker D, Das S.   +5 more
europepmc   +1 more source

Total score of the computer vision syndrome questionnaire predicts refractive errors and binocular vision anomalies. [PDF]

Int J Ophthalmol
Alhassan M, Samman T, Badukhen H, Alrashed M, Alabdulkader B, Almutleb E, Alqahtani T, Almustanyir A.   +7 more
europepmc   +1 more source

A novel univariate legendre polynomial method for probabilistic failure load prediction in composite open-hole laminate. [PDF]

Sci Rep
Li M, Yuan B, Li F, Fang Y, Zhu X.
europepmc   +1 more source

Erratum to Machine Learning-Based Detection of HbS and HbC Carriers in the UK General Population. [PDF]

EJHaem
europepmc   +1 more source

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Bias-Correction Errors-in-Variables Hammerstein Model Identification

IEEE transactions on industrial electronics (1982. Print), 2023
In this paper, a bias-correction least-squares (LS) algorithm is proposed for identifying block- oriented errors-in-variables nonlinear Hammerstein (EIV- Hammerstein) systems.
Jie Hou, Hao Su, Chengpu Yu, Fengwei Chen, Penghua Li   +4 more
semanticscholar   +1 more source

EXTENDING THE CLASSICAL NORMAL ERRORS-IN-VARIABLES MODEL

Econometrica, 1980
IT IS WELL KNOWN that least-squares estimates of the coefficients of a regression equation are inconsistent if any of the regressors are measured with error. The nature of these inconsistencies has been examined by Aigner [1], Blomqvist [2], Chow [3], Levi [5], McCallum [6], and Wickens [10] for the case in which a single regressor is subject to ...
Steven G. Garber, Steven M. Klepper
semanticscholar   +2 more sources

Identification in the Linear Errors in Variables Model

Econometrica, 1983
Consider the following multiple linear regression model with errors in variables: \(y_ j=\xi^ T\!_ j\beta +\epsilon_ j\), \(x_ j=\xi_ j+\nu_ j\), \(j=1,...,n\), where \(\xi_ j\), \(x_ j\), \(\nu_ j\), and \(\beta\) are k-vectors, \(y_ j\), \(\epsilon_ j\) are scalars. The \(\xi_ j\) are unobserved variables: instead the \(x_ j\) are observed.
A. Kapteyn, T. Wansbeek
semanticscholar   +3 more sources

The Errors-in-Variables Model

, 1986
S. Hylleberg
semanticscholar   +2 more sources

Robust estimation in the errors-in-variables model

Biometrika, 1989
An errors-in-variables model in linear regression is considered. The model describes data consisting of \((p+1)\)-tuples \(x_ 1,...,x_ n\) with \(x_ i=X_ i+\epsilon_ i\) and \(a_ 0'X_ i=b_ 0\), where \(X_ i\) and \(\epsilon_ i\) are nonobservable independent random vectors and \(a_ 0\) is a vector of length one. Orthogonal regression determines a and b
R. Zamar
semanticscholar   +3 more sources