270,903 research outputs found
A generic algorithm for reducing bias in parametric estimation
A general iterative algorithm is developed for the computation
of reduced-bias parameter estimates in regular statistical models through
adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published
previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the
adjusted score approach to bias reduction in any model for which the first-
order bias of the maximum likelihood estimator has already been derived.
The method is tested by application to a logit-linear multiple regression
model with beta-distributed responses; the results confirm the effectiveness
of the new algorithm, and also reveal some important errors in the existing
literature on beta regression
Fast Iterative Graph Computation: A Path Centric Approach
Abstract—Large scale graph processing represents an inter-esting challenge due to the lack of locality. This paper presents PathGraph for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of tree-based partitions and use an path-centric computation rather than vertex-centric or edge-centric computation. Our parallel computation model significantly improves the memory and disk locality for performing iterative computation algorithms. Second, we design a compact storage that further maximize sequential access and minimize random access on storage media. Third, we implement the path-centric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential updates for vertices in each partition tree. The experimental results show that the path-centric approach outperforms vertex-centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs
An Improved Combinatorial Polynomial Algorithm for the Linear Arrow-Debreu Market
We present an improved combinatorial algorithm for the computation of
equilibrium prices in the linear Arrow-Debreu model. For a market with
agents and integral utilities bounded by , the algorithm runs in time. This improves upon the previously best algorithm of Ye by a
factor of \tOmega(n). The algorithm refines the algorithm described by Duan
and Mehlhorn and improves it by a factor of \tOmega(n^3). The improvement
comes from a better understanding of the iterative price adjustment process,
the improved balanced flow computation for nondegenerate instances, and a novel
perturbation technique for achieving nondegeneracy.Comment: to appear in SODA 201
Computation of saddle type slow manifolds using iterative methods
This paper presents an alternative approach for the computation of trajectory
segments on slow manifolds of saddle type. This approach is based on iterative
methods rather than collocation-type methods. Compared to collocation methods,
that require mesh refinements to ensure uniform convergence with respect to
, appropriate estimates are directly attainable using the method of
this paper. The method is applied to several examples including: A model for a
pair of neurons coupled by reciprocal inhibition with two slow and two fast
variables and to the computation of homoclinic connections in the
FitzHugh-Nagumo system.Comment: To appear in SIAM Journal of Applied Dynamical System
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