http://biblioteca.universia.net/verColeccion.do?id=8885 Mostrando recursos 1 - 20 de 110 es http://biblioteca.universia.net/img/logotipo.jpg http://biblioteca.universia.net/ http://biblioteca.universia.net/ficha.do?id=22611461 The rich set of interactions between individuals in the society results in complex community structure, capturing highly connected circles of friends, families, or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper under... Palla, Gergely; Barabasi, Albert-Laszlo; Vicsek, Tamas http://biblioteca.universia.net/ficha.do?id=25595556 The paper deals with the study of labor market dynamics, and aims to characterize its equilibriums and possible trajectories. The theoretical background is the theory of the segmented labor market. The main idea is that this theory is well adapted to interpret the observed trajectories, due to the heterogeneity of the work situations. Letrémy, Patrick; Cottrell, Marie; Gaubert, Patrice; Rynkiewicz, Joseph http://biblioteca.universia.net/ficha.do?id=25597799 The estimation of missing input vector elements in real time processing applications requires a system that possesses the knowledge of certain characteristics such as correlations between variables, which are inherent in the input space. Computational intelligence techniques and maximum likelihood techniques do possess such characteristics and as a result are important for imputation of missing data. This paper compares two approaches to the problem of missing data estimation. The first techn... Nelwamondo, Fulufhelo V.; Mohamed, Shakir; Marwala, Tshilidzi http://biblioteca.universia.net/ficha.do?id=25598407 In this paper we develop a scientific approach to control inter-country conflict. This system makes use of a neural network and a feedback control approach. It was found that by controlling the four controllable inputs: Democracy, Dependency, Allies and Capability simultaneously, all the predicted dispute outcomes could be avoided. Furthermore, it was observed that controlling a single input Dependency or Capability also avoids all the predicted conflicts. When the influence of each input var... Marwala, Tshilidzi; Lagazio, Monica; Tettey, Thando http://biblioteca.universia.net/ficha.do?id=25656902 The authors apply three methods of prospective modelling to high resolution georeferenced land cover data in a Mediterranean mountain area: GIS approach, non linear parametric model and neuronal network. Land cover prediction to the latest known date is used to validate the models. In the frame of spatial-temporal dynamics in open systems results are encouraging and comparable. Correct prediction scores are about 73 %. The results analysis focuses on geographic location, land cover categories... Paegelow, Martin; Villa, Nathalie; Cornez, Laurence; Ferraty, Frédéric; Ferré, Louis; Sarda, Pascal http://biblioteca.universia.net/ficha.do?id=25657016 Using former maps, geographers intend to study the evolution of the land cover in order to have a prospective approach on the future landscape; predictions of the future land cover, by the use of older maps and environmental variables, are usually done through the GIS (Geographic Information System). We propose here to confront this classical geographical approach with statistical approaches: a linear parametric model (polychotomous regression modeling) and a nonparametric one (multilayer per... Villa, Nathalie; Paegelow, Martin; Olmedo, Maria T. Camacho; Cornez, Laurence; Ferraty, Frédéric; Ferré, Louis; Sarda, Pascal http://biblioteca.universia.net/ficha.do?id=25657190 Bivariate linear mixed models are useful when analyzing longitudinal data of two associated markers. In this paper, we present a bivariate linear mixed model including random effects or first-order auto-regressive process and independent measurement error for both markers. Codes and tricks to fit these models using SAS Proc MIXED are provided. Limitations of this program are discussed and an example in the field of HIV infection is shown. Despite some limitations, SAS Proc MIXED is a useful t... Thiébaut, Rodolphe; Jacqmin-Gadda, Hélène; Chêne, Geneviève; Leport, Catherine; Commenges, Daniel http://biblioteca.universia.net/ficha.do?id=25657191 Longitudinal studies could be complicated by left-censored repeated measures. For example, in Human Immunodeficiency Virus infection, there is a detection limit of the assay used to quantify the plasma viral load. Simple imputation of the limit of the detection or of half of this limit for left-censored measures biases estimations and their standard errors. In this paper, we review two likelihood-based methods proposed to handle left-censoring of the outcome in linear mixed model. We show how... Thiébaut, Rodolphe; Jacqmin-Gadda, Hélène http://biblioteca.universia.net/ficha.do?id=25658596 This article considers the application of particle filtering to continuous-discrete optimal filtering problems, where the system model is a stochastic differential equation, and noisy measurements of the system are obtained at discrete instances of time. It is shown how the Girsanov theorem can be used for evaluating the likelihood ratios needed in importance sampling. It is also shown how the methodology can be applied to a class of models, where the driving noise process is lower in the dim... Särkkä, Simo; Sottinen, Tommi http://biblioteca.universia.net/ficha.do?id=25659551 The problem of large-scale simultaneous hypothesis testing is re-visited. Bagging and subagging procedures are put forth with the purpose of improving the discovery power of the tests. The procedures are implemented in both simulated and real data. It is shown that bagging and subagging significantly improve power at the cost of a small increase in false discovery rate with the proposed `maximum contrast' subagging having an edge over bagging, i.e., yielding similar power but significantly sm... Politis, Dimitris N. http://biblioteca.universia.net/ficha.do?id=25659753 We consider the problem of binary classification where one can, for a particular cost, choose not to classify an observation. We present a simple proof for the oracle inequality for the excess risk of structural risk minimizers using a lasso type penalty. Wegkamp, Marten http://biblioteca.universia.net/ficha.do?id=25660035 This paper compares the Maximum-likelihood method and Bayesian method for finite element model updating. The Maximum-likelihood method was implemented using genetic algorithm while the Bayesian method was implemented using the Markov Chain Monte Carlo. These methods were tested on a simple beam and an unsymmetrical H-shaped structure. The results show that the Bayesian method gave updated finite element models that predicted more accurate modal properties than the updated finite element model... Marwala, Tshilidzi; Mdlazi, Lungile; Sibisi, Sibusiso http://biblioteca.universia.net/ficha.do?id=25660775 Using predictive adaptive arithmetic coding and the Minimum Description Length principle, we derive an efficient tool for model selection problems : the RIC information criterion. We then present an extension of these coding techniques to non-parametrical estimation of a distribution and illustrate it on the gray scales histogram of an image. Key-words : Information criteria, MDL, model selection, non-parametrical estimation, histograms. Coq, Guilhem; Alata, Olivier; Arnaudon, Marc; Olivier, Christian http://biblioteca.universia.net/ficha.do?id=25661253 We present a quantitative analysis of throwing ability for major league outfielders and catchers. We use detailed game event data to tabulate success and failure events in outfielder and catcher throwing opportunities. We attribute a run contribution to each success or failure which are tabulated for each player in each season. We use four seasons of data to estimate the overall throwing ability of each player using a Bayesian hierarchical model. This model allows us to shrink individual play... Carruth, Matthew; Jensen, Shane T. http://biblioteca.universia.net/ficha.do?id=25662918 Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. Thi... Airoldi, Edoardo M; Blei, David M; Fienberg, Stephen E; Xing, Eric P http://biblioteca.universia.net/ficha.do?id=25663075 We propose the variable selection procedure incorporating prior constraint information into lasso. The proposed procedure combines the sample and prior information, and selects significant variables for responses in a narrower region where the true parameters lie. It increases the efficiency to choose the true model correctly. The proposed procedure can be executed by many constrained quadratic programming methods and the initial estimator can be found by least square or Monte Carlo method. T... Song, Shurong Zheng; Guodong; Shi, Ning-Zhong http://biblioteca.universia.net/ficha.do?id=25663277 This paper presents a dynamic linear model for modeling hourly ozone concentrations over the eastern United States. That model, which is developed within an Bayesian hierarchical framework, inherits the important feature of such models that its coefficients, treated as states of the process, can change with time. Thus the model includes a time--varying site invariant mean field as well as time varying coefficients for 24 and 12 diurnal cycle components. This cost of this model's great flexibi... Dou, Yiping; Le, Nhu D; Zidek, James V http://biblioteca.universia.net/ficha.do?id=25664014 Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse data. This line of work shows that $\ell_1$-regularized least squares regression can accurately estimate a sparse linear model from $n$ noisy examples in $p$ dimensions, even if $p$ is much larger than $n$. In this paper we study a variant of this problem where the original $n$ input variables are compressed by a ra... Zhou, Shuheng; Lafferty, John; Wasserman, Larry http://biblioteca.universia.net/ficha.do?id=25664338 The problem of the definition and the estimation of generative models based on deformable templates from raw data is of particular importance for modelling non aligned data affected by various types of geometrical variability. This is especially true in shape modelling in the computer vision community or in probabilistic atlas building for Computational Anatomy (CA). A first coherent statistical framework modelling the geometrical variability as hidden variables has been given by Allassonni\`... Allassonniere, Stéphanie; Kuhn, Estelle; Trouvé, Alain http://biblioteca.universia.net/ficha.do?id=25664908 A Bayesian approach is used to estimate the covariance matrix of Gaussian data. Ideas from Gaussian graphical models and model selection are used to construct a prior for the covariance matrix that is a mixture over all decomposable graphs. For this prior the probability of each graph size is specified by the user and graphs of equal size are assigned equal probability. Most previous approaches assume that all graphs are equally probable. We show empirically that the prior that assigns equal ... Armstrong, Helen; Carter, Christopher K.; Wong, Kevin F.; Kohn, Robert