least-squares regression techniques
SPECTROMETRIC DETERMINATION OF SOME HEAVY METALS IN COSMETIC PRODUCTS FOUND BY PRINCIPAL COMPONENT REGRESSION AND PARTIAL LEAST SQUARES METHODS
... Several techniques such as X-ray fluorescence [2], atomic fluorescence spectrometry [3], atomic absorption spectrometry [4] , ...spectrophotometry techniques [6,7], due to the resulting experimental ...
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Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy
... statistical techniques such as principal components regression (PCR) (Chang, Laird, Mausbach and Hurburgh , 2001; Islam, Singh, McBratney, 2003; Mouazen, Kuang and De Baerdemaeker, 2010), partial ...
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Comparing objective measures of environmental supports for pedestrian travel in adults
... count regression models, to avoid the inefficient and biased estimates that may result from applying ordinary least-squares ...ordinary least-squares regres- ...aggregation ...
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Ordinary least squares regression of ordered categorical data: inferential implications for practice
... 1.0000000009948218, the predicted probability of always using antimicrobials is 0.2856. A more complete model was also constructed using model selection procedures with all possible covariates. More specifically, ...
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Model structure selection in powertrain calibration and control
... selection techniques based on stepwise regression and orthogonal least squares can be applied successfully on the engine identification process and produced both good polynomial ARX and NARX ...
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Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
... Many supervised machine learning problems are naturally cast as the minimization of a smooth function defined on a Euclidean space. This includes least-squares regression, lo- gistic ...
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Some Sharp Performance Bounds for Least Squares Regression with L 1 Regularization
... It was pointed out in [1] that Lasso and the Dantzig selector are quite similar, and the authors presented a simultaneous analysis of both. Since the explicit parameter estimation bounds in [1] are with the case k = supp ...
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Design, optimization, and validation of chemometrics-assisted spectrophotometric methods for simultaneous determination of etodolac and thiocolchicoside in pharmaceuticals
... partial least squares (PLS), principal component regression (PCR), and inverse least squares (ILS) was developed for the simultaneous analysis of ET and TC without any separation ...
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An Assessment and Comparison of Common Software Cost Estimation Modeling Techniques
... Based on the above selection criteria, we considered the following modeling techniques: ordinary least-squares regression (OLS), a standard Analysis of Variance approach[r] ...
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Functional Analysis of Chemometric Data
... by least square approximation, in the case of noisy observed data, or interpolation when not committed error in the ...the techniques of principal com- ponent regression (PCR) and partial ...
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The relationship between entrepreneurial orientation, human resource management practices, organizational innovation and managerial ties to SME performance
... Partial least squares structural equation modeling (PLS- SEM) techniques using SmartPLS. The internal predictors of business performance in small firms: A logistic regression a[r] ...
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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
... RR techniques are well known shrinkage estimators designed to deal with multicollinearity (see, ...the least squares estimators of the regression ...the regression coefficients that are ...
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New approaches in estimating linear regression model parameters in the presence of multicollinearity and outliers
... linear regression models using real data and simulated ...Ridge Regression (RR), Principal Component Regression (PCR) and ordinary least squares (OLS) are discussed in ...linear ...
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RuitingYang_MSIS_Paper_finalVersion.pdf
... ordinary least squares regression, ridge regression, and LASSO regression models perform well, and predictors such as a talk’s number of language translations, average Internet ...
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Sparse least squares support vector regression for nonstationary systems
... recursive least squares may be insufficient to track the data change due to an overrigid model structure; or the re-estimating model structurally can be computationally expensive for real time ...common ...
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Impact of estimation techniques on regression analysis: an application to survey data on child nutritional status in five African countries
... Ordinary least squares (OLS) regression (which we label Model AI) is applied using the SPSS statistical ...weighted least squares regression, performed using ...
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An Introduction to Partial Least Squares Regression
... multivariate techniques such as principal components regression and maximum re- dundancy ...partial least squares and related modeling ...
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Characterization and Estimation of Dates Palm Trees in an Urban Area Using GIS-Based Least-Squares Model and Minimum Noise Fraction Images
... the least-squares multiple regression and GIS techniques to find suitable predictors from the set of variables such as original bands of Landsat-8, Minimum Noise Fraction (MNF) transformation, ...
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International Journal of Emerging Technology and Advanced Engineering
... mean squares (LMS) algorithms are class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal ...
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Convergence rate for the moving least squares learning with dependent sampling
... The least-squares (LS) method is an important global approximate method based on the regular or concentrated data sample ...moving least-squares (MLS) method was introduced by McLain in [4] to ...
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