This paper describes and analyses a novel distributed implementation of the simulated annealing algorithm to find a good solution to the travelling salesman problem. The implementation runs on a linear chain of proces...
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This paper describes and analyses a novel distributed implementation of the simulated annealing algorithm to find a good solution to the travelling salesman problem. The implementation runs on a linear chain of processors driven by a host processor, which plays only a supervisory role, so that the bulk of processing takes place on the chain and the efficiency of the algorithm remains high as the number of processors is increased.
We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation ...
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We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.
Presents an alternative method of testing population differentiation based on nonparametric procedure. Probability test for population differentiation; Comparison with permutation methods; Testing of the W90 method.
Presents an alternative method of testing population differentiation based on nonparametric procedure. Probability test for population differentiation; Comparison with permutation methods; Testing of the W90 method.
We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not r...
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We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright (C) 2003 John Wiley Sons, Ltd.
The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment co...
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The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online.
Neutron scattering, specific-heat, and magnetization measurements on both powders and single crystals reveal that Dy2Ir2O7 realizes the fragmented monopole crystal state in which antiferromagnetic order and a Coulomb ...
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Neutron scattering, specific-heat, and magnetization measurements on both powders and single crystals reveal that Dy2Ir2O7 realizes the fragmented monopole crystal state in which antiferromagnetic order and a Coulomb phase spin liquid coinhabit. The measured residual entropy is that of a hard-core dimer liquid, as predicted. Inclusion of Coulomb interactions allows for a quantitative description of both the thermodynamic data and the magnetization dynamics, with the energy scale given by deconfined defects in the emergent ionic crystal. Our data reveal low-energy excitations, as well as a large distribution of energy barriers down to low temperatures, while the magnetic response to an applied field suggests that domain wall pinning is important, results that call for further theoretical modeling.
This paper deals with the well known bi-variate Weibull distribution developed by Marshall and Olkin. In the light of prior information, this paper derives the posterior distribution and performs Markov chain Monte Ca...
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This paper deals with the well known bi-variate Weibull distribution developed by Marshall and Olkin. In the light of prior information, this paper derives the posterior distribution and performs Markov chain Monte Carlo methods to obtain posterior based inferences. This paper also checks the sensitivity of posterior estimates by changing the prior variances followed by Bayesian prediction using sample-based approaches. Numerical illustrations are provided for real as well as simulated data sets.
Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Ba...
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Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.
We study the transient behavior of damage propagation in the two-dimensional spin-1 Blume-Capel model using Monte Carlo simulations with metropolis dynamics. We find that, for a particular region in the second-order t...
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We study the transient behavior of damage propagation in the two-dimensional spin-1 Blume-Capel model using Monte Carlo simulations with metropolis dynamics. We find that, for a particular region in the second-order transition regime of the crystal field-temperature phase diagram of the model, the average Hamming distance decreases exponentially with time in the weakly damaged system. Additionally, its rate of decay appears to depend linearly on a number of Hamiltonian parameters, namely the crystal field, temperature, applied magnetic field, but also on the amount of damage. Finally, a comparative study using metropolis and Glauber dynamics indicates a slower decay rate of the average Hamming distance for the Glauber protocol.
This paper describes a Bayesian approach to mixture modelling and a method based on predictive distribution to determine the number of components in the mixtures. The implementation is done through the use of the Gibb...
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This paper describes a Bayesian approach to mixture modelling and a method based on predictive distribution to determine the number of components in the mixtures. The implementation is done through the use of the Gibbs sampler. The method is described through the mixtures of normal and gamma distributions. Analysis is presented in one simulated and one real data example. The Bayesian results are then compared with the likelihood approach for the two examples.
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