683 research outputs found

    JMASM16: Pseudo-Random Number Generation In R For Some Univariate Distributions

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    An increasing number of practitioners and applied researchers started using the R programming system in recent years for their computing and data analysis needs. As far as pseudo-random number generation is concerned, the built-in generator in R does not contain some important univariate distributions. In this article, complementary R routines that could potentially be useful for simulation and computation purposes are provided

    kk-MLE: A fast algorithm for learning statistical mixture models

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    We describe kk-MLE, a fast and efficient local search algorithm for learning finite statistical mixtures of exponential families such as Gaussian mixture models. Mixture models are traditionally learned using the expectation-maximization (EM) soft clustering technique that monotonically increases the incomplete (expected complete) likelihood. Given prescribed mixture weights, the hard clustering kk-MLE algorithm iteratively assigns data to the most likely weighted component and update the component models using Maximum Likelihood Estimators (MLEs). Using the duality between exponential families and Bregman divergences, we prove that the local convergence of the complete likelihood of kk-MLE follows directly from the convergence of a dual additively weighted Bregman hard clustering. The inner loop of kk-MLE can be implemented using any kk-means heuristic like the celebrated Lloyd's batched or Hartigan's greedy swap updates. We then show how to update the mixture weights by minimizing a cross-entropy criterion that implies to update weights by taking the relative proportion of cluster points, and reiterate the mixture parameter update and mixture weight update processes until convergence. Hard EM is interpreted as a special case of kk-MLE when both the component update and the weight update are performed successively in the inner loop. To initialize kk-MLE, we propose kk-MLE++, a careful initialization of kk-MLE guaranteeing probabilistically a global bound on the best possible complete likelihood.Comment: 31 pages, Extend preliminary paper presented at IEEE ICASSP 201

    Replicative Use of an External Model in Simulation Variance Reduction

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    The use of control variates is a well-known variance reduction technique for discrete event simulation experiments. Currently, internal control variates are used almost exclusively by practitioners and researchers when using control variates. The primary objective of this study is to explore the variance reduction achieved by the replicative use of an external, analytical model to generate control variates. Performance for the analytical control variates is compared to the performance of typical internal and external control variates for both an open and a closed queueing network. Performance measures used are confidence interval width reduction, realized coverage, and estimated Mean Square Error. Results of this study indicate analytical control variates achieve comparable confidence interval width reduction with internal and external control variates. However, the analytical control variates exhibit greater levels of estimated bias. Possible causes and remedies for the observed bias are discussed and areas for future research and use of analytical control variates conclude the study

    Copula based multisite model for daily precipitation simulation

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    Robust Estimation Of Multivariate Failure Data With Time-Modulated Frailty

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    A time-modulated frailty model is proposed for analyzing multivariate failure data. The effect of frailties, which may not be constant over time, is discussed. We assume a parametric model for the baseline hazard, but avoid the parametric assumption for the frailty distribution. The well-known connection between survival times and Poisson regression model is used. The parameters of interest are estimated by generalized estimating equations (GEE) or by penalized GEE. Simulation studies show that the procedure is successful to detect the effect of time-modulated frailty. The method is also applied to a placebo controlled randomized clinical trial of gamma interferon, a study of chronic granulomatous disease (CGD)

    Aproximações estatísticas para somas de variáveis aleatórias correlacionadas dos tipos Rayleigh e exponencial com aplicação a esquemas de combinação de diversidade  

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    Orientador: José Cândido Silveira Santos FilhoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Somas de variáveis aleatórias são amplamente aplicadas em sistemas de comunicação sem fio. Exemplos incluem equalização linear, detecção de sinais, fenômenos de interferência e esquemas de combinação de diversidade. No entanto, a formulação exata para as funções estatísticas dessas somas, como a função densidade de probabilidade e a função distribuição acumulada, requer em geral um tratamento matemático complicado, o que tem motivado a busca por soluções aproximadas mais simples. Apesar de haver várias propostas de aproximação disponíveis na literatura, muitas das quais obtidas usando-se a tradicional técnica de casamento de momentos, elas não oferecem um bom ajuste em regime de alta relação sinal-ruído. Sabe-se, porém, que essa é uma região primordial para a análise de desempenho de sistemas de comunicação em termos de métricas importantes como taxa de erro de bit e probabilidade de interrupção. Mais recentemente, com o intuito de contornar essa limitação, foi proposta uma nova técnica promissora conhecida como casamento de assíntotas, capaz de fornecer aproximações para estatísticas de somas de variáveis aleatórias positivas com um ótimo ajuste em regime de alta relação sinal-ruído. Ainda assim, essa técnica foi inicialmente implementada apenas para o caso de somas de variáveis independentes, não sendo até então aplicável para somas de variáveis correlacionadas. Neste trabalho, uma nova análise assintótica é proposta, a partir da qual é possível generalizar o uso do casamento de assíntotas para o caso correlacionado. A análise proposta é ilustrada para somas de variáveis Rayleigh e somas de variáveis exponenciais com correlação e parâmetros de desvanecimento arbitrários. Além disso, deduzem-se expressões assintóticas em forma fechada com o intuito de obter novas aproximações simples e precisas em regime de alta relação sinal-ruído. Como exemplos de aplicação, esquemas práticos de combinação de diversidade são abordados, quais sejam, combinação por ganho igual e combinação por razão máxima. Por fim, resultados numéricos mostram o excelente desempenho das aproximações propostas em comparação com as aproximações obtidas via casamento de momentosAbstract: Sums of random variables are widely applied to wireless communications systems. Examples include linear equalization, signal detection, interference phenomena, and diversity-combining schemes. However, the exact formulation for the statistical functions of these sums, such as the probability density function and the cumulative distribution function, requires in general a complicated mathematical treatment, which has motivated the search for simple approximate solutions. Although there are several approximate proposals available in the literature, many of which obtained through the traditional moment-matching technique, they do not offer a good fit under the regime of high signal-to-noise ratio. It is well-known that this regime is a paramount region for the performance analysis of communications systems in terms of important metrics such as bit error rate and outage probability. More recently, in order to circumvent this limitation, a new promising technique known as asymptotic matching was proposed, capable of providing approximations for statistics of the sum of random variables with an excellent fit under the regime of high signal-to-noise ratio. Even so, this technique was initially proposed for the sum of mutually independent variables only, and thus it has not been applicable to sums of correlated variables. In this work, a novel asymptotic analysis is proposed, from which it is possible to generalize the application of asymptotic matching to the correlated case. The proposed analysis is illustrated for sums of Rayleigh and sums of exponential variables with arbitrary correlation and arbitrary fading parameters. Furthermore, closed-form asymptotic expressions are derived in order to obtain new simple and precise approximations under the regime of high signal-to-noise ratio. As application examples, practical diversity-combining schemes are addressed, namely, equal-gain combining and maximal-ratio combining. Finally, numerical results show the excellent performance of the proposed approximations in comparison to the approximations obtained via moment matchingMestradoTelecomunicações e TelemáticaMestre em Engenharia ElétricaCAPE

    Distributions generated by perturbation of symmetry with emphasis on a multivariate skew tt distribution

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    A fairly general procedure is studied to perturbate a multivariate density satisfying a weak form of multivariate symmetry, and to generate a whole set of non-symmetric densities. The approach is general enough to encompass a number of recent proposals in the literature, variously related to the skew normal distribution. The special case of skew elliptical densities is examined in detail, establishing connections with existing similar work. The final part of the paper specializes further to a form of multivariate skew tt density. Likelihood inference for this distribution is examined, and it is illustrated with numerical examples.Comment: full-length version of the published paper, 31 pages with 9 figure
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