@article {8890,
	title = {Spatio-temporal Modelling of Temperature Fields in the Pacific Northwest},
	journal = {arXiv:1604.00572},
	year = {2016},
	month = {04/2016},
	keywords = {Complex terrain, environmental sciences, Gaussian process, nonstationarity, temperature},
	url = {https://arxiv.org/abs/1604.00572},
	author = {Casquilho-Resende, C. M. and Le, N. D. and Zidek, J.V.}
}
@article { ISI:000174685600010,
	title = {Estimating and depicting the structure of a distribution of random functions},
	journal = {BIOMETRIKA},
	volume = {89},
	number = {1},
	year = {2002},
	month = {MAR},
	pages = {145-158},
	publisher = {BIOMETRIKA TRUST},
	type = {Article},
	address = {UNIV COLLEGE LONDON GOWER ST-BIOMETRIKA OFFICE, LONDON WC1E 6BT, ENGLAND},
	abstract = {We suggest a nonparametric approach to making inference about the structure of distributions in a potentially infinite-dimensional space, for example a function space, and displaying information about that structure. It is suggested that the simplest way of presenting the structure is through modes and density ascent lines, the latter being the projections into the sample space of the curves of steepest ascent up the surface of a functional-data density. Modes are always points in the sample space, and ascent lines are always one-parameter structures, even when the sample space is determined by an infinite number of parameters. They are therefore relatively easily depicted. Our methodology is based on a functional form of an iterative data-sharpening algorithm.},
	keywords = {bandwidth, cluster analysis, Functional data analysis, Gaussian process, generalised Fourier expansion, Karhunen-Loeve expansion, kernel methods, line of steepest ascent, mode, nonparametric density estimation, tree diagram},
	issn = {0006-3444},
	doi = {10.1093/biomet/89.1.145},
	author = {Hall, P and Heckman, NE}
}
