Reconstruction of foliations from directional information
Abstract
In many areas of science, especially geophysics, geography and
meteorology, the data are often directions or axes rather than
scalars or unrestricted vectors. Directional statistics considers
data which are mainly unit vectors lying in two- or
three-dimensional space (R² or R³). One
way in which directional data arise is as normals to foliations. A
(codimension-1) foliation of {R}^{d} is a system
of non-intersecting (d-1)-dimensional surfaces filling out the
whole of {R}^{d}. At each point z of {R}^{d}, any given codimension-1 foliation determines a
unit vector v normal to the surface through z.
The problem considered here is that of reconstructing the foliation
from observations ({z}{i}, {v}{i}), i=1,...,n. One
way of doing this is rather similar to fitting smooth splines to
data. That is, the reconstructed foliation has to be as close to the
data as possible, while the foliation itself is not too rough. A
tradeoff parameter is introduced to control the balance between
smoothness and
closeness. The approach used in this thesis is to take the surfaces to be
surfaces of constant values of a suitable real-valued function h
on {R}^{d}. The problem of reconstructing a foliation is
translated into the language of Schwartz distributions and a deep
result in the theory of distributions is used to give the
appropriate general form of the fitted function h. The model
parameters are estimated by a simplified Newton method. Under appropriate distributional assumptions on v{1},...,v{n}, confidence regions for the true normals
are developed and estimates of concentration are given.
Type
Thesis, PhD Doctor of Philosophy
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