Nous y introduisons une nouvelle classe de distributions bivariées de type Marshall-Olkin, la distribution Erlang bivariée. La transformée de Laplace, les moments et les densités conditionnelles y ...
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Nous y introduisons une nouvelle classe de distributions bivariées de type Marshall-Olkin, la distribution Erlang bivariée. La transformée de Laplace, les moments et les densités conditionnelles y sont obtenus. Les applications potentielles en assurance-vie et en finance sont prises en considération. Les estimateurs du maximum de vraisemblance des paramètres sont calculés par l''algorithme Espérance-Maximisation. Ensuite, notre projet de recherche est consacré à l''étude des processus de risque multivariés, qui peuvent être utiles dans l''étude des problèmes de la ruine des compagnies d''assurance avec des classes dépendantes. Nous appliquons les résultats de la théorie des processus de Markov déterministes par morceaux afin d''obtenir les martingales exponentielles, nécessaires pour établir des bornes supérieures calculables pour la probabilité de ruine, dont les expressions sont intraitables.%%%%In this contribution, we introduce a new class of bivariate distributions of Marshall-Olkin type, called bivariate Erlang distributions. The Laplace transform, product moments and conditional densities are derived. Potential applications of bivariate Erlang distributions in life insurance and finance are considered. Further, our research project is devoted to the study of multivariate risk processes, which may be useful in analyzing ruin problems for insurance companies with a portfolio of dependent classes of business. We apply results from the theory of piecewise deterministic Markov processes in order to derive exponential martingales needed to establish computable upper bounds of the ruin probabilities, as their exact expressions are intractable
This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble o...
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This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framework of the compound classification (CC) decision rule. To develop as uncorrelated as possible classification procedures, the estimates of statistical parameters of classifiers are carried out according to different approaches (i.e., multilayer perceptron neural networks, radial basis functions neural networks, and k-nearest neighbour technique). The outputs provided by different classifiers are combined according to three standard stratcaies extended to the compound classification case (i.e., Majority voting, Bayesian average, and Bayesian,weighted average). Experiments, carried out on a multitemporal. remote-sensing data set, confirm the effectiveness of the proposed system. (C) 2004 Elsevier B.V. All rights reserved.
We prove that noise can speed convergence in the backpropagation algorithm. The proof consists of two separate results. The first result proves that the backpropagation algorithm is a special case of the generalized E...
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ISBN:
(纸本)9781467361293;9781467361286
We prove that noise can speed convergence in the backpropagation algorithm. The proof consists of two separate results. The first result proves that the backpropagation algorithm is a special case of the generalized expectationmaximization (EM) algorithm for iterative maximum likelihood estimation. The second result uses the recent EM noise benefit to derive a sufficient condition for backpropagation training. The noise adds directly to the training data. A noise benefit also applies to the deep bidirectional pre-training of the neural network as well as to the backpropagation training of the network. The geometry of the noise benefit depends on the probability structure of the neurons at each layer. Logistic sigmoidal neurons produce a forbidden noise region that lies below a hyperplane. Then all noise on or above the hyperplane can only speed convergence of the neural network. The forbidden noise region is a sphere if the neurons have a Gaussian signal or activation function. These noise benefits all follow from the general noise benefit of the EM algorithm. Monte Carlo sample means estimate the population expectations in the EM algorithm. We demonstrate the noise benefits using MNIST digit classification.
The traditional expectation-maximization (EM) algorithm is a general purpose algorithm for maximum likelihood estimation in problems with incomplete data. Several variants of the algorithm exist to estimate the parame...
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ISBN:
(纸本)9781728100562
The traditional expectation-maximization (EM) algorithm is a general purpose algorithm for maximum likelihood estimation in problems with incomplete data. Several variants of the algorithm exist to estimate the parameters of phase-type distributions (PHDs), a widely used class of distributions in performance and dependability modeling. EM algorithms are typical offline algorithms because they improve the likelihood function by iteratively running through a fixed sample. Nowadays data can be generated online in most systems such that offline algorithms seem to be outdated in this environment. This paper proposes an online EM algorithm for parameter estimation of PHDs. In contrast to the offline version, the online variant adds data immediately when it becomes available and includes no iteration. Different variants of the algorithms are proposed that exploit the specific structure of subclasses of PHDs like hyperexponential, hyper-Erlang or acyclic PHDs. The algorithm furthermore incorporates current methods to detect drifts or change points in a data stream and estimates a new PHD whenever such a behavior has been identified. Thus, the resulting distributions can be applied for online model prediction and for the generation of inhomogeneous PHDs as an extension of inhomogeneous Poisson processes. Numerical experiments with artificial and measured data streams show the applicability of the approach.
Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies m...
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In this paper we developed the estimation implementation of the generalized hyperbolic multivariate (GH) distribution with a non-fixed Bessel function. The covariance matrix estimated through the GH distribution compl...
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Young people's place in the labor market has been a topic of interest to the European Union and national governments for many years. This study analyzes young people who are Not in Employment nor in Education or T...
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Young people's place in the labor market has been a topic of interest to the European Union and national governments for many years. This study analyzes young people who are Not in Employment nor in Education or Training (NEET) and Youth Unemployment (YU) in the European Union member states, through data collected over a period of sixteen years, considering the influence of some macroeconomic factors through an hidden Markov model. This approach is based on maximum likelihood estimation of the model parameters, and provides a dynamic classification of the countries into clusters representing different levels of the phenomena. We discover three clusters of countries, and we show that whereas Italy was the worst performing country in terms of both NEETs and YU, the Czech Republic was the best performing country in reducing NEETs, and Poland and Slovakia were the best performing in reducing YU.
In this paper, unlike the commonly considered theoretical scenario in clustering, wherein data attributes are accurately presented, we research how successful clustering can be indeed performed in situations wherein b...
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ISBN:
(纸本)9781728182292
In this paper, unlike the commonly considered theoretical scenario in clustering, wherein data attributes are accurately presented, we research how successful clustering can be indeed performed in situations wherein before being grouped the data have to be quantized, that is, represented with smaller accuracy. This describes situation wherein before being grouped the data are compressed due to their transmission through the bandwidth-limited channel. We consider scenario wherein the data modeled with the one-dimensional two-component Gaussian mixture model (GMM) are uniformly quantized and, after that, grouped by using the expectation-maximization (EM) algorithm. We study how the number of quantization levels and the quantizer support limits influence on the results of clustering.
Digital image forensics has become a very important research topic This paper proposes a method to detect the forgery of digital image by (I) computing the interpolated coefficient for the Images using expectation-max...
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ISBN:
(纸本)9783642104664
Digital image forensics has become a very important research topic This paper proposes a method to detect the forgery of digital image by (I) computing the interpolated coefficient for the Images using expectation-maximization (EM) algorithm, (2) generating the probability map. (3) obtaining the frequency spectrum of the probability map, (4) determining whether an image has been tampered based on the periodicity characteristics of the spectrum The experimental results show that our approach is effective to detect Mice different image forgeries (a) an-brush or brush strokes, (b) different blurring filters. and (c) composite image taken from different cameras
We propose a multivariate approach for the estimation of intergenerational transition matrices. Our methodology is grounded on the assumption that individuals' social status is unobservable and must be estimated. ...
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We propose a multivariate approach for the estimation of intergenerational transition matrices. Our methodology is grounded on the assumption that individuals' social status is unobservable and must be estimated. In this framework, parents and offspring are clustered on the basis of the observed levels of income and occupational categories, thus avoiding any discretionary rule in the definition of class boundaries. The resulting transition matrix is a function of the posterior probabilities of parents and young adults of belonging to each class. Estimation is carried out via maximum likelihood by means of an expectation-maximization algorithm. We illustrate the proposed method using National Longitudinal Survey Data from the United States in the period 1978-2006.
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