Measurement methods comparison with errors-in-variables regressions. From horizontal to vertical OLS regression, review and new perspectives
BG Francq, BB Govaerts - Chemometrics and Intelligent Laboratory …, 2014 - Elsevier
This paper summarizes and confronts the relationships between six well-known regressions
applied in the context of measurement methods comparison with or without replicated data …
applied in the context of measurement methods comparison with or without replicated data …
Method of moments estimation in linear regression with errors in both variables
J Gillard - Communications in Statistics-Theory and Methods, 2014 - Taylor & Francis
Recently, in this journal, there has been revised attention on estimating the parameters of
the errors in variables, linear structural model. For example, O'Driscoll and Ramirez (2011) …
the errors in variables, linear structural model. For example, O'Driscoll and Ramirez (2011) …
Exploitation of error correlation in a large analysis validation: GlobCurrent case study
An assessment of variance in ocean current signal and noise shared by in situ observations
(drifters) and a large gridded analysis (GlobCurrent) is sought as a function of day of the …
(drifters) and a large gridded analysis (GlobCurrent) is sought as a function of day of the …
Filter Estimator based on the probability distribution
RU Parrazalez, RP Orozco, MTZ Alvarez… - IEEE …, 2025 - ieeexplore.ieee.org
In this paper, we propose a model of a parameter estimator filter for Black Box-type
Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its …
Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its …
Fitting data to a multiple structural measurement errors model
R Al Dibi'i, R Abdul Rahman… - … in Statistics-Simulation …, 2024 - Taylor & Francis
This paper proposes two new estimation methods to fit a multiple structural measurement
error model when all variables are subject to errors. The new estimation methods were …
error model when all variables are subject to errors. The new estimation methods were …
Methods of fitting straight lines where both variables are subject to measurement error
J Gillard, T Iles - Current Clinical Pharmacology, 2009 - ingentaconnect.com
In this paper errors in variables methods for fitting straight lines to data are reviewed. In
these methods the x and y variables are both assumed to be subject to measurement error …
these methods the x and y variables are both assumed to be subject to measurement error …
Hyperbolic confidence bands of errors-in-variables regression lines applied to method comparison studies
BG Francq, BB Govaerts - Journal de la Société Française de …, 2014 - numdam.org
This paper focuses on the confidence bands of errors-in-variables regression lines applied
to method comparison studies. When comparing two measurement methods, the goal is …
to method comparison studies. When comparing two measurement methods, the goal is …
Analytic and stochastic methods of structure parameter estimation
M Kuznetsov, A Tokmakova, V Strijov - Informatica, 2016 - content.iospress.com
The paper presents analytic and stochastic methods of structure parameters estimation for a
model selection problem. Structure parameters are covariance matrices of parameters of …
model selection problem. Structure parameters are covariance matrices of parameters of …
PCA-based antenna impedance estimation in Rayleigh fading channels
S Wu, BL Hughes - ar** Estimation Method for Fitting Multiple Structural Regression Model when all Variables are Subject to Errors
RA Rahman, A Al-Nasser - Pakistan Journal of Statistics and Operation …, 2024 - pjsor.com
Abstract The Measurement Error Model (MEM) is employed to fit the relationship between
two or more variables when all variables are subject to measurement errors. In the specific …
two or more variables when all variables are subject to measurement errors. In the specific …