Search
Search Results
-
Variable selection using axis-aligned random projections for partial least-squares regression
In high-dimensional data modeling, variable selection plays a crucial role in improving predictive accuracy and enhancing model interpretability...
-
Locally sparse and robust partial least squares in scalar-on-function regression
We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our...
-
Partial Least Squares Path Modeling Basic Concepts, Methodological Issues and Applications
Now in its second edition, this edited book presents recent progress and techniques in partial least squares path modeling (PLS-PM), and provides a...
-
Least Squares: Regression and ANOVA
Some fundamental concepts relating to linear models are introduced. Least squares estimation is discussed as a method for computing estimates of...
-
Modeling of soil organic matter using Sentinel-1 SAR and partial least squares (PLS) regression
The determination of soil properties, in addition to requiring great human effort, also involves a number of technical activities of high financial...
-
Sparse functional partial least squares regression with a locally sparse slope function
The partial least squares approach has been particularly successful in spectrometric prediction in chemometrics. By treating the spectral data as...
-
Preprocessing of Laser-Induced Breakdown Spectra of Low Alloy Steels and Cast Irons Using Partial Least-Squares Regression Analysis
Regression models for the analysis of manganese, chromium, nickel, copper, silicon, vanadium, titanium, and aluminum were constructed using partial...
-
Capturing functional connectomics using Riemannian partial least squares
For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions...
-
Locality-Preserving Partial Least Squares Regression
This chapter proposes another nonlinear PLS method, named as locality-preserving partial least squares (LPPLS), which embeds the nonlinear...
-
Image classification based on weighted nonconvex low-rank and discriminant least squares regression
Classifiers based on least squares regression (LSR) are effective in multi-classification tasks. However, there are two main problems that greatly...
-
Linear Regression Analysis Using Least Squares
Regression analysis is a statistical technique for determining or modeling the relationship between a response/dependent variable and one or more...
-
Penalized Least Squares Classifier: Classification by Regression Via Iterative Cost-Sensitive Learning
Least squares estimate that can directly obtain the analytical solution to minimize the mean square error (MSE) is one of the most effective...
-
Compressed Least Squares Algorithm of Continuous-Time Linear Stochastic Regression Model Using Sampling Data
In this paper, the authors consider a sparse parameter estimation problem in continuous-time linear stochastic regression models using sampling data....
-
Ordinary Least Squares Regression
This chapter introduces simple and multiple linear regression and their typical estimator, ordinary least squares. Linear regression is a common...
-
Large deviations for randomly weighted least squares estimator in a nonlinear regression model
In this work, we introduce the random weighting method to the nonlinear regression model and study the asymptotic properties for the randomly...
-
An Improved Regression Partial Least Squares Method for Quality-Related Process Monitoring of Industrial Control Systems
Partial least squares (PLS) is a widely used and effective method in the field of fault detection. However, due to the fact that the standard PLS...
-
Quality-related Fault Detection Based on Approximate Kernel Partial Least Squares Method
The kernel partial least squares (KPLS) method has been widely used in quality-related fault detection since it can acquire the features of the...
-
Distributed Least Squares Algorithm of Continuous-Time Stochastic Regression Model Based on Sampled Data
In this paper, the authors consider the distributed adaptive identification problem over sensor networks using sampled data, where the dynamics of...
-
Neural network ensemble model for prediction of erythrocyte sedimentation rate (ESR) using partial least squares regression
The erythrocyte sedimentation rate (ESR) is a non-specific blood test for determining inflammatory conditions. However, the long measurement time...
-
Soil-moisture-index spectrum reconstruction improves partial least squares regression of spectral analysis of soil organic carbon
Accurate remote estimation of the soil organic carbon (SOC) content can be useful for site-specific soil management and precision agriculture....
