Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
28,443
result(s) for
"Least squares"
Sort by:
Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review
Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.
Journal Article
This fast car can move faster
The relevance and prominence of the partial least squares structural equation modeling (PLS-SEM) method has recently increased in higher education research, especially in explanatory and predictive studies. We therefore first aim to assess previous PLS-SEM applications by providing a systematic review; second, we aim to highlight and summarize important guidelines for conducting a rigorous PLS-SEM analysis of the current state of results reporting in higher education journals. Specifically, this study focuses on empirical PLS-SEM applications in 14 major higher education journals indexed in the Thomson Reuters Web of Science and in the Elsevier-Scopus databases between 1999 and 2018. We initially identified 49 relevant papers published in 10 higher education journals. Based on these papers’ generally followed guidelines, we thereafter identified various issues related to data screening, model characteristics, measurement model evaluation, structural model evaluation, and the application of state-of-the-art PLS-SEM advanced methods requiring particular attention. Furthermore, we recommend recent guidelines to improve PLS-SEM applications and practices, besides providing specific suggestions regarding utilizing the method’s strength in terms of relevant higher education research questions. Our findings remind researchers, reviewers, and journal editors to remain vigilant, should help them avoid inaccuracies in future publications, and ensure rigor.
Journal Article
LSWAVE: a MATLAB software for the least-squares wavelet and cross-wavelet analyses
The least-squares wavelet analysis (LSWA) is a robust method of analyzing any type of time/data series without the need for editing and preprocessing of the original series. The LSWA can rigorously analyze any non-stationary and equally/unequally spaced series with an associated covariance matrix that may have trends and/or datum shifts. The least-squares cross-wavelet analysis complements the LSWA in the study of the coherency and phase differences of two series of any type. A MATLAB software package including a graphical user interface is developed for these methods to aid researchers in analyzing pairs of series. The package also includes the least-squares spectral analysis, the antileakage least-squares spectral analysis, and the least-squares cross-spectral analysis to further help researchers study the components of interest in a series. We demonstrate the steps that users need to take for a successful analysis using three examples: two synthetic time series, and a Global Positioning System time series.
Journal Article
Mediation analysis in partial least squares path modeling
Purpose
Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues.
Design/methodology/approach
This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM).
Findings
This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects.
Originality/value
The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.
Journal Article
Coupled-least-squares identification for multivariable systems
This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least-squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C-LS algorithm does not involve the matrix inversion and requires less computationally efforts than the multivariable RLS algorithm, and that the parameter estimates given by the C-LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.
Journal Article
Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls
Linear least squares estimators are widely used in diffusion MRI for the estimation of diffusion parameters. Although adding proper weights is necessary to increase the precision of these linear estimators, there is no consensus on how to practically define them. In this study, the impact of the commonly used weighting strategies on the accuracy and precision of linear diffusion parameter estimators is evaluated and compared with the nonlinear least squares estimation approach.
Simulation and real data experiments were done to study the performance of the weighted linear least squares estimators with weights defined by (a) the squares of the respective noisy diffusion-weighted signals; and (b) the squares of the predicted signals, which are reconstructed from a previous estimate of the diffusion model parameters.
The negative effect of weighting strategy (a) on the accuracy of the estimator was surprisingly high. Multi-step weighting strategies yield better performance and, in some cases, even outperformed the nonlinear least squares estimator.
If proper weighting strategies are applied, the weighted linear least squares approach shows high performance characteristics in terms of accuracy/precision and may even be preferred over nonlinear estimation methods.
•Linear least squares estimators are widely used in diffusion MRI.•Weighting of the linear least squares estimator is needed to improve the precision.•Different weighting strategies are routinely used.•The actual accuracy of linear estimators strongly depends on the weight definition.•The squares of the noisy diffusion-weighted signals should not be used as weights.
Journal Article
Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management
Purpose
Structural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used to estimate complex cause-effect relationship models with latent variables as the most salient research methods across a variety of disciplines, including knowledge management (KM). Following the path initiated by different domains in business research, this paper aims to examine how PLS-SEM has been applied in KM research, also providing some new guidelines how to improve PLS-SEM report analysis.
Design/methodology/approach
To ensure an objective way to analyse relevant works in the field of KM, this study conducted a systematic literature review of 63 publications in three SSCI-indexed and specific KM journals between 2015 and 2017.
Findings
Our results show that over the past three years, a significant amount of KM works has empirically used PLS-SEM. The findings also suggest that in light of recent developments of PLS-SEM reporting, some common misconceptions among KM researchers occurred mainly related to the reasons for using PLS-SEM, the purposes of PLS-SEM analysis, data characteristics, model characteristics and the evaluation of the structural models.
Originality/value
This study contributes to that vast KM literature by documenting the PLS-SEM-related problems and misconceptions. Therefore, it will shed light for better reports in PLS-SEM studies in the KM field.
Journal Article
Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle
This study derives a least-squares-based iterative algorithm and a gradient-based iterative algorithm for Hammerstein systems using the decomposition-based hierarchical identification principle. The simulation results confirm that the proposed two algorithms can give satisfactory identification accuracies and the least-squares-based iterative algorithm has faster convergence rates than the gradient-based iterative algorithm.
Journal Article