Journal ArticleDOI
PLS-regression: a basic tool of chemometrics
TLDR
PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 2001-10-28. It has received 7861 citations till now. The article focuses on the topics: Partial least squares regression.read more
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Journal ArticleDOI
QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov,Eugene N. Muratov,Eugene N. Muratov,Denis Fourches,Alexandre Varnek,Igor I. Baskin,Mark T. D. Cronin,John C. Dearden,Paola Gramatica,Yvonne C. Martin,Roberto Todeschini,Viviana Consonni,Victor E. Kuz’min,Richard D. Cramer,Romualdo Benigni,Chihae Yang,James F. Rathman,Lothar Terfloth,Johann Gasteiger,Ann M. Richard,Alexander Tropsha +20 more
TL;DR: In this paper, the authors provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive quantitative structure-activity relationship models.
Journal ArticleDOI
Macroscopic mass and energy balance of a pilot plant anaerobic bioreactor operated under thermophilic conditions.
Teodoro Espinosa-Solares,John Bombardiere,Mark Chatfield,Max Domaschko,Michael Easter,David A. Stafford,Saul Castillo-Angeles,Nehemias Castellanos-Hernandez +7 more
TL;DR: Results suggest some changes to the pilot plant configuration are necessary to reduce power consumption although maximizing biodigester performance, and a modification of the typical continuous stirred tank reactor is a promising process being relatively stable and owing to its capability to manage considerable amounts of residuals at low operational cost.
Journal ArticleDOI
A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.
Journal ArticleDOI
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods
Joseph F. Hair,G. Tomas M. Hult,Christian M. Ringle,Christian M. Ringle,Marko Sarstedt,Marko Sarstedt,Kai Oliver Thiele +6 more
TL;DR: Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based, and while both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.
Journal ArticleDOI
Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.
TL;DR: For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation and are presented with small numerical examples and typical applications in neuroimaging.
References
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Book
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
Book
A User's Guide to Principal Components
TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
Journal ArticleDOI
A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
Bradley Efron,Gail Gong +1 more
TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.