@article {8972,
	title = {A characterization of elliptical distributions and some optimality properties of principal components for functional data},
	journal = {Journal of Multivariate Analysis},
	volume = {131},
	year = {2014},
	pages = {254 - 264},
	keywords = {Principal components},
	issn = {0047-259X},
	doi = {10.1016/j.jmva.2014.07.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0047259X14001638},
	author = {Graciela Boente and Mat{\'\i}as Salibi{\'a}n Barrera and David E. Tyler}
}
@article { ISI:000322829800011,
	title = {VISUALIZING GENETIC CONSTRAINTS},
	journal = {ANNALS OF APPLIED STATISTICS},
	volume = {7},
	number = {2},
	year = {2013},
	month = {JUN},
	pages = {860-882},
	publisher = {INST MATHEMATICAL STATISTICS},
	type = {Article},
	address = {3163 SOMERSET DR, CLEVELAND, OH 44122 USA},
	abstract = {Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, evolutionary biologists gain important insights from these low variation directions. Specifically, they are interested in directions of low genetic variability that are biologically interpretable. These directions are called genetic constraints and indicate directions in which a trait cannot evolve through selection. Here, we propose studying the subspace spanned by low variance principal components by determining vectors in this subspace that are simplest. Our method and accompanying graphical displays enhance the biologist{\textquoteright}s ability to visualize the subspace and identify interpretable directions of low genetic variability that align with simple directions.},
	keywords = {evolutionary biology, genetic constraints, Principal components},
	issn = {1932-6157},
	doi = {10.1214/12-AOAS603},
	author = {Gaydos, Travis L. and Heckman, Nancy E. and Kirkpatrick, Mark and Stinchcombe, J. R. and Schmitt, Johanna and Kingsolver, Joel and Marron, J. S.}
}
@article { ISI:000240158700035,
	title = {Principal components analysis based on multivariate MM estimators with fast and robust bootstrap},
	journal = {JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION},
	volume = {101},
	number = {475},
	year = {2006},
	month = {SEP},
	pages = {1198-1211},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {1429 DUKE ST, ALEXANDRIA, VA 22314 USA},
	abstract = {We consider robust principal components analysis (PCA) based on multivariate MM estimators. We first study the robustness and efficiency of these estimators, particularly in terms of eigenvalues and eigenvectors. We then focus on inference procedures based on a fast and robust bootstrap for MM estimators. This method is an alternative to the approach based on the asymptotic distribution of the estimators and can also be used to assess the stability of the principal components. A formal consistency proof for the bootstrap method is given, and its finite-sample performance is investigated through simulations. We illustrate the use of the robust PCA and the bootstrap inference on a real dataset.},
	keywords = {bootstrap, inference, MM-estimators, Principal components, Robustness},
	issn = {0162-1459},
	doi = {10.1198/016214506000000096},
	author = {Salibian-Barrera, Mat{\'\i}as and Van Aelst, Stefan and Willems, Gert}
}
