The problem of simulating from distributions with intractable normalizing constants has received much attention in recent literature. In this article, we propose an asymptotic algorithm, the so-called double Metropoli...
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The problem of simulating from distributions with intractable normalizing constants has received much attention in recent literature. In this article, we propose an asymptotic algorithm, the so-called double Metropolis-Hastings (MH) sampler, for tackling this problem. Unlike other auxiliary variable algorithms, the double MH sampler removes the need for exact sampling, the auxiliary variables being generated using MH kernels, and thus can be applied to a wide range of problems for which exact sampling is not available. For the problems for which exact sampling is available, it can typically produce the same accurate results as the exchange algorithm, but using much less CPU time. The new method is illustrated by various spatial models.
We formulate the famous Scarf Lemma in terms of oiks. This lemma has two fundamental applications in game and graph theory. In 1967, Scarf derived from it core-solvability of balanced cooperative games. Recently, it w...
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This article proposes a new Bayesian Markov chain Monte Carlo (MCMC) methodology for estimation of a wide class of multidimensional jump-diffusion models. Our approach is based on the closed-form (CF) likelihood appro...
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This article proposes a new Bayesian Markov chain Monte Carlo (MCMC) methodology for estimation of a wide class of multidimensional jump-diffusion models. Our approach is based on the closed-form (CF) likelihood approximations of Ait-Sahalia (2002, 2008). The CF likelihood approximation does not integrate to 1;it is very close to 1 when in the center of the distribution but can differ markedly from 1 when far in the tails. We propose an MCMC algorithm that addresses the problems that arise when the CF approximation is applied in a Bayesian context. The efficacy of our approach is demonstrated in a simulation study of the Cox-Ingersoll-Ross and Heston models and is applied to two well-known datasets.
Motivated by the computer search algorithms for constructing two-level supersaturated designs by Heavlin and Finnegan (1993), Li and Wu (1997), Nguyen (1996), Lejeune (2003) and Gupta, Parsad, Kole and Bhar (2008), th...
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Motivated by the computer search algorithms for constructing two-level supersaturated designs by Heavlin and Finnegan (1993), Li and Wu (1997), Nguyen (1996), Lejeune (2003) and Gupta, Parsad, Kole and Bhar (2008), this paper develops an algorithm to generate multi-level supersaturated designs. Popular E (f(NOD)) and E (chi(2)) criterion have been used as a measure of non-orthogonality for the designs generated. The algorithm also ensures that no two columns in the designs generated are fully aliased. A catalogue of 120 optimal supersaturated designs for different number of factors m, design runs n, with 5 <= n <= 16 runs, and different number of factor levels q, with 3 <= q <= 6, has been prepared. All the designs generated are f(NOD)-optimal;some designs are chi(2)-optimal
The idea of "Lemke pivoting in a family of oiks (Euler complexes)" generalizes, and abstracts to pure combinatorics, the Lemke-Howson exchange algorithm for finding a Nash equilibrium in bimatrix games, as w...
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We present a class of instances of the existence of a second object of a specified type, in fact, of an even number of objects of a specified type, which generalizes the existence of an equilibrium for bimatrix games....
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Let T be a triangulated surface given by the list of vertex-triples of its triangles, called rooms. A room-partitioning of T is a subset R of the rooms such that each vertex of T is in exactly one room in *** prove th...
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A new semi-active method for multi-mode vibration control using the nonlinear synchronized switch damping (SSD) approach based on a displacement switching threshold is proposed in this paper. Several extensions of the...
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A new semi-active method for multi-mode vibration control using the nonlinear synchronized switch damping (SSD) approach based on a displacement switching threshold is proposed in this paper. Several extensions of the SSD approach, including SSDI (SSD on inductance), SSDV (SSD on a voltage source), enhanced SSDV, and adaptive SSDV, have been developed to improve the control of the single-mode vibration, but the weakness of the SSD approach for multi-modal vibration control has not been solved. In all these extensions of the SSD approach, the switch is controlled by the same algorithm, that is, it reverses the voltage of the piezoelectric element at all extrema of displacement. This switching algorithm is effective in single-mode control, but it leads to over-frequent switching in multi-mode control. In the method proposed in this study, an improved switching algorithm based on a displacement threshold, which prevents the switch in the shunt circuit from over-frequent on-and-off actions and accordingly increases the converted energy to improve the control performance, is proposed. The switching algorithm is applied to an SSDI system and used in the vibration damping of a beam with two excited modes. Compared to the classical SSDI approach, the control performance of the first mode is improved from 3.7 to 18.2 dB, but that of the second mode is slightly worse, having changed from 3.46 to 2.6.
Supersaturated designs are an increasingly popular tool for screening factors in the presence of effect sparsity. The advantage of this class of designs over resolution III factorial designs or Plackett-Burman designs...
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Supersaturated designs are an increasingly popular tool for screening factors in the presence of effect sparsity. The advantage of this class of designs over resolution III factorial designs or Plackett-Burman designs is that n, the number of runs, can be substantially smaller than the number of factors. m. A limitation associated with most supersaturated designs produced thus far is that the capability of these designs for estimating g active effects has not been discussed. In addition to exploring this capability, we develop a new class of model-robust supersaturated designs that, for a given n and m. maximizes the number g of active effects that can be estimated simultaneously. The capabilities of model-robust supersaturated designs for model discrimination are assessed using a model-discrimination criterion, the subspace angle. Finally, we introduce the class of partially supersaturated designs, intended for use when we require a specific subset of m, core factors to be estimable, and the sparsity of effects principle applies to the remaining (m - m(1)) factors. (C) 2008 Elsevier B.V. All rights reserved.
We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate...
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We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E(s(2)) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks. (c) 2007 Elsevier B.V. All rights reserved.
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