1,130 research outputs found
Fast computation of Tukey trimmed regions and median in dimension
Given data in , a Tukey -trimmed region is the set of
all points that have at least Tukey depth w.r.t. the data. As they are
visual, affine equivariant and robust, Tukey regions are useful tools in
nonparametric multivariate analysis. While these regions are easily defined and
interpreted, their practical use in applications has been impeded so far by the
lack of efficient computational procedures in dimension . We construct
two novel algorithms to compute a Tukey -trimmed region, a na\"{i}ve
one and a more sophisticated one that is much faster than known algorithms.
Further, a strict bound on the number of facets of a Tukey region is derived.
In a large simulation study the novel fast algorithm is compared with the
na\"{i}ve one, which is slower and by construction exact, yielding in every
case the same correct results. Finally, the approach is extended to an
algorithm that calculates the innermost Tukey region and its barycenter, the
Tukey median
Local bilinear multiple-output quantile/depth regression
A new quantile regression concept, based on a directional version of Koenker
and Bassett's traditional single-output one, has been introduced in [Ann.
Statist. (2010) 38 635-669] for multiple-output location/linear regression
problems. The polyhedral contours provided by the empirical counterpart of that
concept, however, cannot adapt to unknown nonlinear and/or heteroskedastic
dependencies. This paper therefore introduces local constant and local linear
(actually, bilinear) versions of those contours, which both allow to
asymptotically recover the conditional halfspace depth contours that completely
characterize the response's conditional distributions. Bahadur representation
and asymptotic normality results are established. Illustrations are provided
both on simulated and real data.Comment: Published at http://dx.doi.org/10.3150/14-BEJ610 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Quasiconvex Programming
We define quasiconvex programming, a form of generalized linear programming
in which one seeks the point minimizing the pointwise maximum of a collection
of quasiconvex functions. We survey algorithms for solving quasiconvex programs
either numerically or via generalizations of the dual simplex method from
linear programming, and describe varied applications of this geometric
optimization technique in meshing, scientific computation, information
visualization, automated algorithm analysis, and robust statistics.Comment: 33 pages, 14 figure
Approximating Tverberg Points in Linear Time for Any Fixed Dimension
Let P be a d-dimensional n-point set. A Tverberg-partition of P is a
partition of P into r sets P_1, ..., P_r such that the convex hulls conv(P_1),
..., conv(P_r) have non-empty intersection. A point in the intersection of the
conv(P_i)'s is called a Tverberg point of depth r for P. A classic result by
Tverberg implies that there always exists a Tverberg partition of size n/(d+1),
but it is not known how to find such a partition in polynomial time. Therefore,
approximate solutions are of interest.
We describe a deterministic algorithm that finds a Tverberg partition of size
n/4(d+1)^3 in time d^{O(log d)} n. This means that for every fixed dimension we
can compute an approximate Tverberg point (and hence also an approximate
centerpoint) in linear time. Our algorithm is obtained by combining a novel
lifting approach with a recent result by Miller and Sheehy (2010).Comment: 14 pages, 2 figures. A preliminary version appeared in SoCG 2012.
This version removes an incorrect example at the end of Section 3.
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