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Principal components for allometric analysis
American Journal of Physical Anthropology, 1983AbstractLogarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BXa. Both techniques depend on high correlation between variables.
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Introduction to Principal Components Analysis
PM&R, 2014Principal components analysis (PCA) is a powerful statistical tool that can help researchers analyze datasets with many highly related predictors. PCA is a data reduction technique— that is, it reduces a larger set of predictor variables to a smaller set with minimal loss of information.
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On coMADs and Principal Component Analysis
2019Principal Component Analysis (PCA) is a popular method for linear dimensionality reduction. It is often used to discover hidden correlations or to facilitate the interpretation and visualization of data. However, it is liable to suffer from outliers. Strong outliers can skew the principal components and as a consequence lead to a higher reconstruction ...
Daniyal Kazempour +2 more
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A Discussion of Principal Component Analysis
Journal of Analytical Toxicology, 1985H, van der Voet, J P, Franke
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Cross-Validatory Choice of the Number of Components From a Principal Component Analysis
Technometrics, 1982W J Krzanowski
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ROBPCA: A New Approach to Robust Principal Component Analysis
Technometrics, 2005Mia Hubert, Peter J Rousseeuw
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