Book Chapter10.1007/978-94-007-5824-7_16
Partial Least Squares
Ton J. Cleophas,Aeilko H. Zwinderman +1 more
- 01 Jan 2013
- pp 197-213
938
TL;DR: The authors used Principal Component Analysis (PCA) and partial least square analysis (PLSSA) to reduce large numbers of variables in clinical trials, but they did not consider the relative importance of the separate variables, their interactions and differences in units.
read more
Abstract: Traditional statistical tests are unable to handle a large number of variables. The simplest method to reduce large numbers of variables is the use of add-up scores. But add-up scores do not account for the relative importance of the separate variables, their interactions and differences in units. Principal components analysis and partial least square analysis account all of that, but are virtually unused in clinical trials.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A new criterion for assessing discriminant validity in variance-based structural equation modeling
TL;DR: In this paper, the heterotrait-monotrait ratio of correlations is used to assess discriminant validity in variance-based structural equation modeling. But it does not reliably detect the lack of validity in common research situations.
When to use and how to report the results of PLS-SEM
TL;DR: A comprehensive overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting can be found in this paper, where the authors provide an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLSSEM.
15.4K
An assessment of the use of partial least squares structural equation modeling in marketing research
Joseph F. Hair,Marko Sarstedt,Marko Sarstedt,Christian M. Ringle,Christian M. Ringle,Jeannette A. Mena +5 more
TL;DR: An extensive search in the 30 top ranked marketing journals allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010), and a critical analysis of these articles addresses the following key methodological issues: reasons for using PLS, data and model characteristics, outer and inner model evaluations, and reporting.
7.4K
Predictive model assessment in PLS-SEM: guidelines for using PLSpredict
Galit Shmueli,Marko Sarstedt,Joseph F. Hair,Jun-Hwa Cheah,Hiram Ting,Santha Vaithilingam,Christian M. Ringle +6 more
TL;DR: Clear guidelines for using PLSpredict are offered, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses and the key choices researchers need to make using the procedure are explained.
2.3K
Goodness-of-fit indices for partial least squares path modeling
Jörg Henseler,Marko Sarstedt +1 more
TL;DR: This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices, and estimates PLS path models with simulated data, and contrasts their values with fit indices commonly used in covariance-based structural equation modeling.
References
PLS path modeling
Michel Tenenhaus,Vincenzo Esposito Vinzi,Vincenzo Esposito Vinzi,Yves-Marie Chatelin,Carlo Lauro +4 more
TL;DR: PLS path modeling can be used for analyzing multiple tables so as to be related to more classical data analysis methods used in this field and some new improvements are proposed.
5.8K
Spearman and the origin and development of factor analysis
TL;DR: In this article, it is argued that the fitful progress of factor analysis and its slow and incomplete assimilation into the mainstream of statistical theory can be traced to the lack of a clear idea, until relatively recently, of the role of a model in the development of statistical methods.
72
Modeling microRNA-mRNA Interactions Using PLS Regression in Human Colon Cancer
TL;DR: An alternative bioinformatics approach for predicting miRNA targets in human colon cancer and for reverse engineering the miRNA:mRNA network using inversely related mRNA and miRNA joint expression profiles is identified.
Functional Regression Analysis using an F Test for Longitudinal Data with Large Numbers of Repeated Measures
TL;DR: This paper illustrates how to apply an F test for linear models with functional responses and functional regression analysis to the setting of longitudinal data, and finds the functional F test provides consistent results supported by a mixed-effects linear regression model.
Functional Regression Analysis using an F Test for Longitudinal Data with Large Numbers of Repeated Measures
Xiaowei Yang,Qing Shen,Hongquan Xu,Steven Shoptaw +3 more
- 01 Jul 2005
TL;DR: In this article, the authors apply functional regression analysis to the setting of longitudinal data where intra-subject repeated measures are viewed as discrete samples from an underlying curve with continuous function forms, and the functional F test provides consistent results supported by a random-effects linear regression model.
26
Related Papers (5)
Myung-Hoe Huh,Yong-Bin Lim,Yong-Goo Lee +2 more
- 01 Jan 2008
Takahiro Hoshino,Peter M. Bentler +1 more
- 25 Oct 2011
D. Berger
- 01 Jan 2012
Gary Smith
- 01 Jan 2015