DSpace Collection:http://hdl.handle.net/10174/11172019-10-21T06:18:15Z2019-10-21T06:18:15ZRegularized inversion of flow size distributionAntunes, nelsonPipiras, VladasJacinto, Gonçalohttp://hdl.handle.net/10174/259092019-10-01T05:32:16Z2019-06-16T23:00:00ZTitle: Regularized inversion of flow size distribution
Authors: Antunes, nelson; Pipiras, Vladas; Jacinto, Gonçalo
Abstract: In this paper, we revisit the estimation of the size distribution of packet flows in Internet traffic through an inversion approach for several packet sampling schemes which are based on probabilistic sampling (PS). We first study the statistical properties of the previously introduced inversion estimator in its general form and make connections to the singular value decomposition. This motivates the use of a regularization technique in the estimation of the flow size distribution. More specifically, a penalized weighted least square approach is proposed. We compare theoretically the penalized estimator under simplified assumptions against the (non-penalized) inversion approach in order to explain differences in their statistical behaviors. A data study with two real traces shows that the proposed penalized estimator outperforms the inversion estimator for all sampling schemes, corroborating the theoretical analysis. This work reveals that the simplest sampling schemes based on PS, that do not work with small sampling probabilities under the inversion approach, can be used with the penalized approach. Furthermore, the penalized approach allows considering smaller packet sampling rates for all the other sampling schemes.2019-06-16T23:00:00ZStrong generalized synchronization with a particular relationship R between the coupled systemsGracio, ClaraFernandes, SaraLopes, Luís Máriohttp://hdl.handle.net/10174/258542019-09-19T10:07:46Z2018-01-01T00:00:00ZTitle: Strong generalized synchronization with a particular relationship R between the coupled systems
Authors: Gracio, Clara; Fernandes, Sara; Lopes, Luís Mário
Editors: Efremova, L.S.; Gracio, C.; Lopez-Ruiz, R.; Stepin, A.M.; Sakbaev, V.Z.
Abstract: The question of the chaotic synchronization of two coupled dynamical systems
is an issue that interests researchers in many elds, from biology to psychology, through
economics, chemistry, physics, and many others. The di erent forms of couplings and the
di erent types of synchronization, give rise to many problems, most of them little studied.
In this paper we deal with general couplings of two dynamical systems and we study strong
generalized synchronization with a particular relationship R between them. Our results include
the de nition of a window in the domain of the coupling strength, where there is an exponentially
stable solution, and the explicit determination of this window. In the case of unidirectional or
symmetric couplings, this window is presented in terms of the maximum Lyapunov exponent
of the systems. Examples of applications to chaotic systems of dimension one and two are
presented.2018-01-01T00:00:00ZPractical use of correlation coefficients in the Social SciencesHernández, OscarAlpizar-Jara, Russellhttp://hdl.handle.net/10174/254382019-03-29T11:39:37Z2018-06-30T23:00:00ZTitle: Practical use of correlation coefficients in the Social Sciences
Authors: Hernández, Oscar; Alpizar-Jara, Russell
Editors: Sorto, M.A.; White, A.; Guyot, L.
Abstract: The Pearson correlation coefficient (r) is usually the first measure of association taught at elementary statistics courses. The usual presentation includes scatterplots, computation and interpretation of r, properties, examples, and warnings about inferring causality from high association between two variables. On this last aspect, few introductory textbooks go deeper into the criteria for establishing causation, and there is a lack of convincing examples in the area of the Social Sciences. Although some textbooks give adequate explanations, most of their examples belong to the field of Biostatistics. There is a need to incorporate convincing cases of the practical use of correlation as supporting evidence of causal relationships in the Social Sciences. We contribute with two examples that could be useful for teaching purposes.2018-06-30T23:00:00ZInference for the Evolution in Series of StudiesAreia, A.Mexia, J.Oliveira, Mariahttp://hdl.handle.net/10174/254272019-03-25T17:30:00Z2018-07-14T23:00:00ZTitle: Inference for the Evolution in Series of Studies
Authors: Areia, A.; Mexia, J.; Oliveira, Maria
Abstract: Studies will be matrix triplets (X,Dp,Dn), where the matrix X has a
row per object and a column per variable, while Dp and Dn are
weight matrices for objects and variables, respectively.
Given a series of studies (Xi,Dp,Dn),i=1,…,k, we condense the
matrix triplets into the
, and use spectral analysis
of matrix [ ] with (
) to study
the series evolution.
When we have a series of studies for each treatment of a basis
design we carry out an ANOVA-like inference to study the action
of the factors in the base design on the evolution of the series
associated to the differents treatments.2018-07-14T23:00:00Z