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Olivier Cappé Randal Douc Arnaud Guillin Jean-Michel Marin Christian P. Robert 《Statistics and Computing》2008,18(4):447-459
In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture
importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion.
The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular
mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular
the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.
This work has been supported by the Agence Nationale de la Recherche (ANR) through the 2006–2008 project
’
. Both last authors are grateful to the participants to the BIRS
meeting on “Bioinformatics, Genetics and Stochastic Computation: Bridging the Gap”, Banff, for their comments on an earlier
version of this paper. The last author also acknowledges an helpful discussion with Geoff McLachlan. The authors wish to thank
both referees for their encouraging comments. 相似文献
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Rolando Biscay Lirio Dunia Giniebra Camejo Jean-Michel Loubes Lilian Muñiz Alvarez 《Statistical Methods and Applications》2014,23(2):149-174
In this paper, we propose a data-driven model selection approach for the nonparametric estimation of covariance functions under very general moments assumptions on the stochastic process. Observing i.i.d replications of the process at fixed observation points, we select the best estimator among a set of candidates using a penalized least squares estimation procedure with a fully data-driven penalty function, extending the work in Bigot et al. (Electron J Stat 4:822–855, 2010). We then provide a practical application of this estimate for a Kriging interpolation procedure to forecast rainfall data. 相似文献
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A new methodology for model determination in decomposable graphical Gaussian models (Dawid and Lauritzen in Ann. Stat. 21(3), 1272?C1317, 1993) is developed. The Bayesian paradigm is used and, for each given graph, a hyper-inverse Wishart prior distribution on the covariance matrix is considered. This prior distribution depends on hyper-parameters. It is well-known that the models??s posterior distribution is sensitive to the specification of these hyper-parameters and no completely satisfactory method is registered. In order to avoid this problem, we suggest adopting an empirical Bayes strategy, that is a strategy for which the values of the hyper-parameters are determined using the data. Typically, the hyper-parameters are fixed to their maximum likelihood estimations. In order to calculate these maximum likelihood estimations, we suggest a Markov chain Monte Carlo version of the Stochastic Approximation EM algorithm. Moreover, we introduce a new sampling scheme in the space of graphs that improves the add and delete proposal of Armstrong et al. (Stat. Comput. 19(3), 303?C316, 2009). We illustrate the efficiency of this new scheme on simulated and real datasets. 相似文献
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Jean-Michel Denis 《Sociologie du Travail》2003,45(3):307-325
Since 1988 when it was founded, the Sud-PTT Union has often used law suits as a means of action in dealings with French postal authorities. Law is a full part of its practices. This is surprising coming from a union strongly attached to a protest tradition and so symbolically hostile to any form of institutionalization. What reasons underlie this orientation? The “conflict of rules” undertaken by SUD-PTT must be seen in relation to a major change in contemporary social strife. The “antagonistic posture” is no longer limited to collective actions but extends to more institutionalized strategies. Not just technical or instrumental, this sort of action aims at bringing pressure to bear on norms and, as a consequence, on the codification of social relations and on ways of living together. 相似文献
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Approximate Bayesian computational methods 总被引:3,自引:0,他引:3
Jean-Michel Marin Pierre Pudlo Christian P. Robert Robin J. Ryder 《Statistics and Computing》2012,22(6):1167-1180
Approximate Bayesian Computation (ABC) methods, also known as likelihood-free techniques, have appeared in the past ten years as the most satisfactory approach to intractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions brought on the original ABC algorithm in recent years. 相似文献
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Sahut Jean-Michel Peris-Ortiz Marta Teulon Frédéric 《Journal of Management and Governance》2019,23(4):913-913
Journal of Management and Governance - The original version of this article contained a mistake. The list of the authors was incorrect. The authors of this article are: Jean-Michel Sahut, Marta... 相似文献