174,998 research outputs found
Generalized resolution for orthogonal arrays
The generalized word length pattern of an orthogonal array allows a ranking
of orthogonal arrays in terms of the generalized minimum aberration criterion
(Xu and Wu [Ann. Statist. 29 (2001) 1066-1077]). We provide a statistical
interpretation for the number of shortest words of an orthogonal array in terms
of sums of values (based on orthogonal coding) or sums of squared
canonical correlations (based on arbitrary coding). Directly related to these
results, we derive two versions of generalized resolution for qualitative
factors, both of which are generalizations of the generalized resolution by
Deng and Tang [Statist. Sinica 9 (1999) 1071-1082] and Tang and Deng [Ann.
Statist. 27 (1999) 1914-1926]. We provide a sufficient condition for one of
these to attain its upper bound, and we provide explicit upper bounds for two
classes of symmetric designs. Factor-wise generalized resolution values provide
useful additional detail.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1205 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Word-level Symbolic Trajectory Evaluation
Symbolic trajectory evaluation (STE) is a model checking technique that has
been successfully used to verify industrial designs. Existing implementations
of STE, however, reason at the level of bits, allowing signals to take values
in {0, 1, X}. This limits the amount of abstraction that can be achieved, and
presents inherent limitations to scaling. The main contribution of this paper
is to show how much more abstract lattices can be derived automatically from
RTL descriptions, and how a model checker for the general theory of STE
instantiated with such abstract lattices can be implemented in practice. This
gives us the first practical word-level STE engine, called STEWord. Experiments
on a set of designs similar to those used in industry show that STEWord scales
better than word-level BMC and also bit-level STE.Comment: 19 pages, 3 figures, 2 tables, full version of paper in International
Conference on Computer-Aided Verification (CAV) 201
Sequential Specification Tests to Choose a Model: A Change-Point Approach
Researchers faced with a sequence of candidate model specifications must
often choose the best specification that does not violate a testable
identification assumption. One option in this scenario is sequential
specification tests: hypothesis tests of the identification assumption over the
sequence. Borrowing an idea from the change-point literature, this paper shows
how to use the distribution of p-values from sequential specification tests to
estimate the point in the sequence where the identification assumption ceases
to hold. Unlike current approaches, this method is robust to individual errant
p-values and does not require choosing a test level or tuning parameter. This
paper demonstrates the method's properties with a simulation study, and
illustrates it by application to the problems of choosing a bandwidth in a
regression discontinuity design while maintaining covariate balance and of
choosing a lag order for a time series model
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