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Hermite least squares optimization: a modification of BOBYQA for optimization with limited derivative information
Derivative-free optimization tackles problems, where the derivatives of the objective function are unknown. However, in practical optimization...
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Classification of multivariate functional data on different domains with Partial Least Squares approaches
Classification (supervised-learning) of multivariate functional data is considered when the elements of the random functional vector of interest are...
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Multi-elements linear discriminant analysis of herbaceous and woody plants in southwest china karst region using orthogonal partial least squares model
The karst region in southwest China is one of world’s largest continuous karst landforms in the world, renowned for its unique landscapes and...
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Effective Dimensionality Reduction Using Kernel Locality Preserving Partial Least Squares Discriminant Analysis
Partial least squares discriminant analysis (PLS-DA) is one the popular tool for the analysis of data in chemometrics and bioinformatics. As a...
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Least Squares
In Sect. 10.6 we showed that the least squares (LS) method originates from the maximum likelihood...
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A Comparative Study Between Partial Least Squares and Principal Component Regression for Nondestructive Quantification of Piperine Contents in Black Pepper by Raman Spectroscopy
The aim of this work was to compare principal component regression (PCR) and partial least squares (PLS) regression methods while estimating the...
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Evaluation of groundwater quality for agricultural under different conditions using water quality indices, partial least squares regression models, and GIS approaches
Evaluating grouLindwater quality and associated hydrochemical properties is critical to manage groundwater resources in arid and semiarid...
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SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference
BackgroundThe identification of gene regulatory networks (GRNs) facilitates the understanding of the underlying molecular mechanism of various...
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A Geometric Proximal Gradient Method for Sparse Least Squares Regression with Probabilistic Simplex Constraint
In this paper, we consider the sparse least squares regression problem with probabilistic simplex constraint. Due to the probabilistic simplex...
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Assessment of Land Use Change Impact on Sediment Yield Using SWAT and Partial Least Squares Regression Model
A holistic understanding of the impact of land use/land cover (LC) changes on the sedimentations is important for efficient decisions on sustainable...
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Performance of Genocchi wavelet neural networks and least squares support vector regression for solving different kinds of differential equations
In this study, two numerical methods [(a) artificial neural network method with three layers (input layer, hidden layer, output layer) and (b) least...
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Generalized kernel regularized least squares estimator with parametric error covariance
A two-step estimator of a nonparametric regression function via Kernel regularized least squares (KRLS) with parametric error covariance is proposed....
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Joint sparse least squares via generalized fused lasso penalty for identifying nonlinear dynamical systems
This paper proposes a joint sparse least-square model that utilizes a generalized fused lasso penalty to jointly identify governing equations of...
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A partial least squares approach for function-on-function interaction regression
A partial least squares regression is proposed for estimating the function-on-function regression model where a functional response and multiple...
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Effectiveness of public health spending: Investigating the moderating role of governance using partial least squares structural equation modelling (PLS-SEM)
BackgroundThe link between public health spending (PHS) and population health outcomes (PHO) has been extensively studied. However, in sub-Saharan...
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Analysing spectroscopy data using two-step group penalized partial least squares regression
A statistical challenge to analyse hyperspectral data is the multicollinearity between spectral bands. Partial least squares (PLS) has been...
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Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements
This chapter aims to provide a brief overview of the three primary structural equation modeling approaches, which include partial least squares-path...
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Weighted least squares for archetypal analysis with missing data
Archetypal analysis expresses observations in terms of a limited number of archetypes, defined as convex combinations of observed units. Such...
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Use of Conditional Variational Autoencoders and Partial Least Squares in Solving an Inverse Problem of Spectroscopy
In this article, we consider the solution of an inverse problem of Raman spectroscopy of water-ethanol solutions by artificial neural networks (NN)....