Journal ArticleDOI
PLS-regression: a basic tool of chemometrics
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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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Estimating soil salinity in Pingluo County of China using QuickBird data and soil reflectance spectra
TL;DR: Estimating the soil salinity of Pingluo County, China by a partial least squares regression (PLSR) predictive model was carried out using QuickBird data and soil reflectance spectra and the results indicated that the sensitive bands covered several bands of each optical sensor and these sensors can be used for soilSalinity estimation.
Journal ArticleDOI
Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity
Jeannine Cavender-Bares,Jose Eduardo Meireles,John J. Couture,Matthew A. Kaproth,Clayton C. Kingdon,Aditya Singh,Shawn P. Serbin,Shawn P. Serbin,Esau Zuniga,George Pilz,Philip A. Townsend +10 more
TL;DR: Leaf-level full range spectral data is connected to the hierarchical organization of plant diversity within the oak genus (Quercus) using field and greenhouse experiments in which environmental factors and plant age are controlled to show that spectral data significantly differentiate populations within a species and that spectral similarity is significantly associated with phylogenetic similarity among species.
Journal ArticleDOI
Coordinated multitissue transcriptional and plasma metabonomic profiles following acute caloric restriction in mice
Colin Selman,Nicola D Kerrison,Anisha Cooray,Matthew D.W. Piper,Steven J. Lingard,Richard H. Barton,Eugene Schuster,Eric Blanc,David Gems,Jeremy K. Nicholson,Janet M. Thornton,Linda Partridge,Dominic J. Withers +12 more
TL;DR: Mice undergoing acute CR rapidly adopt many transcriptional and metabolic changes of long-term CR, suggesting that the beneficial effects of CR may require only a short-term reduction in caloric intake.
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A bootstrapping soft shrinkage approach for variable selection in chemical modeling
Baichuan Deng,Baichuan Deng,Baichuan Deng,Yong-Huan Yun,Dong-Sheng Cao,Yu-Long Yin,Yu-Long Yin,Wei-Ting Wang,Hongmei Lu,Qianyi Luo,Yi-Zeng Liang +10 more
TL;DR: A new variable selection method called bootstrapping soft shrinkage (BOSS) method, derived from the idea of weighted bootstrap sampling and model population analysis, which shows that BOSS is promising with improved prediction performance.
Journal ArticleDOI
Prediction of nanoparticles-cell association based on corona proteins and physicochemical properties
TL;DR: It is suggested that QSARs exploration of NP-cell association data, considering the role of both NP protein corona and physicochemical properties, can support the planning and interpretation of toxicity studies and guide the design of NPs for biomedical applications.
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.