Current Issue : April - June Volume : 2013 Issue Number : 2 Articles : 4 Articles
Bezerra et al. 2008 proposed a new method, based on Yule-Walker equations, to estimate the\r\nARMA spectral model. In this paper, a Bayesian approach is developed for this model by using\r\nthe noninformative prior proposed by Jeffreys 1967. The Bayesian computations, simulation via\r\nMarkov Monte Carlo MCMC is carried out and characteristics of marginal posterior distributions\r\nsuch as Bayes estimator and confidence interval for the parameters of the ARMA model are\r\nderived. Both methods are also compared with the traditional least squares and maximum\r\nlikelihood approaches and a numerical illustration with two examples of the ARMA model is\r\npresented to evaluate the performance of the procedures...
We propose the degenerate-generalized likelihood ratio test DGLRT for one-sided composite\r\nhypotheses in cases of independent and dependent observations. The theoretical results show\r\nthat the DGLRT has controlled error probabilities and stops sampling with probability 1 under\r\nsome regularity conditions. Moreover, its stopping boundaries are constants and can be easily\r\ndetermined using the provided searching algorithm. According to the simulation studies, the\r\nDGLRT has less overall expected sample sizes and less relative mean index RMI values in\r\ncomparison with the sequential probability ratio test SPRT and double sequential probability\r\nratio test 2-SPRT. To illustrate the application of it, a real manufacturing data are analyzed....
Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to\r\nshape detection, optimization, and classification in pattern recognition. Similarly, multithreshold\r\nselection has become a critical step for image analysis and computer vision sparking considerable\r\nefforts to design an optimal multi-threshold estimator. This paper presents an algorithm for\r\nmulti-threshold segmentation which is based on the artificial immune systemsAIS technique,\r\nalso known as theclonal selection algorithm CSA. It follows the clonal selection principle\r\nCSP from the human immune system which basically generates a response according to the\r\nrelationship between antigens Ag, that is, patterns to be recognized and antibodies Ab, that\r\nis, possible solutions. In our approach, the 1D histogram of one image is approximated through a\r\nGaussian mixture model whose parameters are calculated through CSA. Each Gaussian function\r\nrepresents a pixel class and therefore a thresholding point. Unlike the expectation-maximization\r\nEM algorithm, the CSA-based method shows a fast convergence and a low sensitivity to\r\ninitial conditions. Remarkably, it also improves complex time-consuming computations commonly\r\nrequired by gradient-based methods. Experimental evidence demonstrates a successful automatic\r\nmulti-threshold selection based on CSA, comparing its performance to the aforementioned wellknown\r\nalgorithms....
We use the bifurcation method of dynamical systems to study the traveling wave solutions for\r\nthe generalized Zakharov equations. A number of traveling wave solutions are obtained. Those\r\nsolutions contain explicit periodic wave solutions, periodic blow-up wave solutions, unbounded\r\nwave solutions, kink profile solitary wave solutions, and solitary wave solutions. Relations of the\r\ntraveling wave solutions are given. Some previous results are extended....
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