Add to ORKGThe prediction uncertainty is studied when using a multivariate partial least squares regression (PLSR) model constructed with reference values that contain a sizeable measurement error. Several approximate expressions for calculating a sample-specific standard error of prediction have been proposed in the literature. In addition, Monte Carlo simulation methods such as the bootstrap and the noise addition method can give an estimate of this uncertainty. In this paper, two approximate expressions are compared with the simulation methods for three near-infrared data sets
The asymptotic expression for the mean-squared prediction error is discussed for the near-unit-root ...
This paper analyzes the performance of linear regression models taking into account usual criteria s...
Prediction under model uncertainty is an important and difficult issue. Traditional prediction metho...
Five methods are compared for assessing the uncertainty in multivariate regression coefficients, nam...
Partial least squares (PLS) regression is commonly used for multivariate calibration of instruments....
Multivariate calibration methods have been applied extensively to the quantitative analysis of Fouri...
Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, c...
Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has ...
While a multitude of expressions has been proposed for calculating sample-specific standard errors o...
Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected ...
Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield con...
Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield con...
In this article we demonstrate that, when evaluating a method for determining prediction intervals, ...
The prediction of spatially and/or temporal varying variates based on observations of these variates...
International audienceMallows's Cp and Akaike information criterion (AIC) are common criteria for se...
The asymptotic expression for the mean-squared prediction error is discussed for the near-unit-root ...
This paper analyzes the performance of linear regression models taking into account usual criteria s...
Prediction under model uncertainty is an important and difficult issue. Traditional prediction metho...
Five methods are compared for assessing the uncertainty in multivariate regression coefficients, nam...
Partial least squares (PLS) regression is commonly used for multivariate calibration of instruments....
Multivariate calibration methods have been applied extensively to the quantitative analysis of Fouri...
Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, c...
Over the last decade, near-infrared spectroscopy, together with the use of chemometrics models, has ...
While a multitude of expressions has been proposed for calculating sample-specific standard errors o...
Model averaging (MA) is a modelling strategy where the uncertainty in the configuration of selected ...
Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield con...
Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield con...
In this article we demonstrate that, when evaluating a method for determining prediction intervals, ...
The prediction of spatially and/or temporal varying variates based on observations of these variates...
International audienceMallows's Cp and Akaike information criterion (AIC) are common criteria for se...
The asymptotic expression for the mean-squared prediction error is discussed for the near-unit-root ...
This paper analyzes the performance of linear regression models taking into account usual criteria s...
Prediction under model uncertainty is an important and difficult issue. Traditional prediction metho...