64,053 research outputs found
A Statistical Toolbox For Mining And Modeling Spatial Data
Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis
Three Simulation Algorithms for Labelled Transition Systems
Algorithms which compute the coarsest simulation preorder are generally
designed on Kripke structures. Only in a second time they are extended to
labelled transition systems. By doing this, the size of the alphabet appears in
general as a multiplicative factor to both time and space complexities. Let
denotes the state space, the transition relation, the
alphabet and the partition of induced by the coarsest simulation
equivalence. In this paper, we propose a base algorithm which minimizes, since
the first stages of its design, the incidence of the size of the alphabet in
both time and space complexities. This base algorithm, inspired by the one of
Paige and Tarjan in 1987 for bisimulation and the one of Ranzato and Tapparo in
2010 for simulation, is then derived in three versions. One of them has the
best bit space complexity up to now,
, while another one has the
best time complexity up to now, . Note the
absence of the alphabet in these complexities. A third version happens to be a
nice compromise between space and time since it runs in
time, with a branching factor generally
far below , and uses
bits
Bisimulations over DLTS in O(m.log n)-time
The well known Hopcroft's algorithm to minimize deterministic complete
automata runs in -time, where is the size of the alphabet and
the number of states. The main part of this algorithm corresponds to the
computation of a coarsest bisimulation over a finite Deterministic Labelled
Transition System (DLTS). By applying techniques we have developed in the case
of simulations, we design a new algorithm which computes the coarsest
bisimulation over a finite DLTS in -time and -space, with
the number of transitions. The underlying DLTS does not need to be complete
and thus: . This new algorithm is much simpler than the two others
found in the literature.Comment: Submitted to DLT'1
Comment: Lancaster Probabilities and Gibbs Sampling
Comment on ``Lancaster Probabilities and Gibbs Sampling'' [arXiv:0808.3852]Comment: Published in at http://dx.doi.org/10.1214/08-STS252A the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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