41,452 research outputs found
A general theory of minimum aberration and its applications
Minimum aberration is an increasingly popular criterion for comparing and
assessing fractional factorial designs, and few would question its importance
and usefulness nowadays. In the past decade or so, a great deal of work has
been done on minimum aberration and its various extensions. This paper develops
a general theory of minimum aberration based on a sound statistical principle.
Our theory provides a unified framework for minimum aberration and further
extends the existing work in the area. More importantly, the theory offers a
systematic method that enables experimenters to derive their own aberration
criteria. Our general theory also brings together two seemingly separate
research areas: one on minimum aberration designs and the other on designs with
requirement sets. To facilitate the design construction, we develop a
complementary design theory for quite a general class of aberration criteria.
As an immediate application, we present some construction results on a weak
version of this class of criteria.Comment: Published at http://dx.doi.org/10.1214/009053604000001228 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating spatial quantile regression with functional coefficients: A robust semiparametric framework
This paper considers an estimation of semiparametric functional
(varying)-coefficient quantile regression with spatial data. A general robust
framework is developed that treats quantile regression for spatial data in a
natural semiparametric way. The local M-estimators of the unknown
functional-coefficient functions are proposed by using local linear
approximation, and their asymptotic distributions are then established under
weak spatial mixing conditions allowing the data processes to be either
stationary or nonstationary with spatial trends. Application to a soil data set
is demonstrated with interesting findings that go beyond traditional analysis.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ480 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
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