Multivariate Partial Least Squares (PLS) Regression
Simultaneous quantification of artesunate and mefloquine in fixed dose combination tablets by multivariate calibration with middle infrared spectroscopy and partial least squares regression
... novel multivariate method for direct simultaneous quantification of mefloquine and artesunate in tablets by diffuse reflectance, middle infrared spectroscopy and partial least squares ...
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Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression
... a multivariate response regression approach with simultaneous variable selection and dimension reduction for the eQTL mapping ...sparse partial least-squares regression ...
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Multivariate Statistical Methodologies Applied in Biomedical Raman Spectroscopy: Assessing the Validity of Partial Least Squares Regression Using Simulated Model Datasets.
... Raman spectroscopy is fast becoming a valuable analytical tool in a number of biomedical scenarios, most notably disease diagnostics. Importantly, the technique has also shown increasing promise in the assessment of drug ...
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Stability indicating chemometric methods for the determination of two cephalosporin drugs by partial least squares and principle component regression multivariate calibration methods
... Two multivariate calibration methods, including principal component regression (PCR) and partial least square (PLS), have been used for the determination of two cephalosporin drugs namely ...
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An Introduction to Partial Least Squares Regression
... other multivariate techniques such as principal components regression and maximum re- dundancy ...performing partial least squares and related modeling ...
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Partial Least Squares (PLS) Regression.
... the multivariate regression for- mula as b Y = TBC T = XB PLS with B PLS = (P T+ )BC T (where P T+ is the Moore-Penrose pseudo-inverse of P T ...this regression is equivalent to principal component ...
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Partial Least Squares Regression in the Social Sciences
... popular and versatile statistical program. We hope that this brief introduction to PLSR will encourage social sciences researchers to continue to learn about this technique and to discover its many uses (e.g., data ...
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Influence properties of partial least squares regression.
... The regression vector is calculated from these latent variables, hence overcoming difficulties in ordinary least squares such as ...alternative multivariate latent variable regression ...
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Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration
... process regression [2] can be applied to multivariate spectro- scopic ...article, least squares Support Vector Machines(LS-SVMs) and Gaus- sian process regression(GPR) are further ...
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Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
... Both multivariate and univariate regression models were ...studied. Multivariate kernel PLS tries to find components that are good predictors for all response ...variables. Multivariate kernel ...
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Principle Component Analysis and Partial Least Squares: Two Dimension Reduction Techniques for Regression
... for multivariate analysis, it is especially critical for multivariate regressions in many P&C insurance-related ...and partial least squares (PLC), for dimension reduction in a case ...
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Keywords Sugarcane, lignin, stalk, partial least squares regression, PLS, variable selection
... The use of chemometric methods to extract informa- tion from multivariate data, such as spectra, can significantly reduce the time, cost, and environmental impact of chem- ical analysis. 5,13–15 In this sense, the ...
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Determination of Hexazinone in Environmental Samples by Uv-Vis Spectrophotometry-Partial Least Squares Regression
... A fast and reliable method is proposed for the determination of HEXA in tap and well water, such as aqueous soil leachates. Organic matter interferences were avoided by means of multivariate calibration using the ...
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Quantification of Prediction Uncertainty for Principal Components Regression and Partial Least Squares Regression
... for partial least squares prediction uncertainty sug- gested in the literature are quite varied, can lead to quite different answers, and often involve doubtful ...ing multivariate calibration ...
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11 Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression
... a multivariate normal distribution with mean μ and covariance Σ, its squared Mahalanobis distance (X μ) T Σ 1 ( X μ) has a χ 2 distribution with D degrees of ...
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Stability indicating analysis of bisacodyl by partial least squares regression, spectral residual augmented classical least squares and support vector regression chemometric models: A comparative study
... these multivariate models from lab to lab under different conditions, generalisation ability of these models and which of them can handle nonlinearities in spectral response ...
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A two-step regression method with connections to partial least squares and the growth curve model
... some multivariate variables is at the core of statistical ...ordinary least squares (OLS) ...the least squares estimator is BLUE (the best linear unbiased ...mean squares error ...
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The degrees of freedom of partial least squares regression
... for Partial Least Squares regression can be a challenging ...of Partial Least Squares ...of Partial Least Squares to matrix decompositions and Krylov ...
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A linearization method for partial least squares regression prediction uncertainty
... 3.3. Simulation: k = 3, a = 2, σ 2 1 = σ 2 2 = 25, σ 2 3 = 1, β 0 = β 1 = β 2 = 1, β 3 = 0, σ 2 ǫ = 0.25, N = 10000. The previous simulation was deliberately chosen to be a difficult case for PLS and it is perhaps not ...
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Partial least squares regression on symmetric positive-definite matrices
... PLS regression is a two-phase ...PLS regression, in which the components are obtained using only the predictor matrix, ...estimated regression model is adjusted using the original response vector and ...
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