Times Series Applied to Study Vitamin D Seasonality in Argentina

In this study, we analyze how vitamin D (VD) serum levels flow with latitude and throughout seasons of the year within a population sample over three years, taking into account that VD is mainly photosynthesized in the skin from sun exposure. Vitamin D levels have been measured in 80,763 patients during 2013, 2014, and 2015. To accomplish the objectives, we first perform some inference tests like two-way Analysis of Variance (ANOVA) followed by post-hoc tests. Secondly, we develop time series techniques including cross correlation calculations. Least than 10% of the sample had healthy VD levels, which should be a fact of public health major concern. The effect of the interaction between the two factors, zones and seasons, was proved by ANOVA. The mean values which are significantly different were determined by post hoc test. Furthermore, we find that mean serum VD levels, measured as 25-hydroxy-VD, follow a seasonal lag pattern of 9 weeks, a delay for minimum and maximum values after the respective equinoxes and daily sunlight duration. Reliable estimates of the population are provided in the present study, since one of the strengths is its huge sample size. We have quantitatively characterized the seasonality of serum vitamin D levels in the Argentine and the seasonal lag pattern has been determined for the study region.

Properties of the Estimators for the Effective Bandwidth in a Generalized Markov Fluid Model

The Generalized Markov Fluid Model (GMFM), introduced in [1], is assumed for modeling sources in the network because it is versatile to describe the traffic fluctuations. In order to estimate resources allocations or in other words the channel occupation of each source, the concept of effective bandwidth proposed by Kelly [2] is used. In this paper, we present a formula for calculating the effective bandwidth, developed for the Generalized Markovian Flow model, which is of particular interest because it allows expressing said magnitude depending on the parameters of the model. We present unbiased estimators for these parameters that can be obtained from real data. The convergence and the consistency of the estimation are studied, and confidence bands are found. Illustrative calculation and performance of the proposed estimators were tested with simulated data and ideal results were obtained.

Facebook Dynamics: Modelling and Statistical Testing

In this work we study virtual social networks known as Facebook. It is used by millions of people worldwide, gathering a combination of virtual elements and real world components. We suggest a probabilistic model to describe the long-term behavior of Facebook. This model includes different friendship connection between profiles, directly or by suggestion. Due to web’s high interactivity level, we simplify the model assuming Markovian dynamic. After the model is established we propose Complete Transversality (CT) communication concept. CT describes people interaction that reflects profile behaviour and leads to estimators that measure this interaction. Then we introduce a weakness version of CT named Segmental Transversality (ST). Within this framework we develop estimators that allow hypothesis testing of CT and ST. And then, in ST context we propose performance measures to address a priori segmentation’s quality.

Effective Bandwidth Estimation in Data Networks: An Analysis for Two Traffic Characterizations

The Generalized Markov Fluid Model(GMFM)is assumed for modeling sources in the network because it is versatile to describe the traffic fluctuations.In order to estimate resources allocations or in other words the channel occupation of each source,the concept of effective bandwidth(EB)proposed by Kelly is used.In this paper we use an expression to determine the EB for this model which is of particular interest because it allows expressing said magnitude depending on the parameters of the model.This paper provides EB estimates for this model applying Kernel Estimation techniques in data networking.In particular we will study two differentiated cases:dispatches following a Gaussian and Exponential distribution.The performance of the proposed method is analyzed using simulated traffic traces generated by Monte Carlo Markov Chain algorithms.The estimation process worked much better in the Gaussian distribution case than in the Exponential one.

Optimal capacity and buffer size estimation under generalized Markov Fluids models and QoS parameters

Quality of service(QoS)for internet trafic management requires good trafic models and good estimation of sharing network resource.A link of a network processes all trafic and it is designed with certain capacity C and buffer size B.A Generalized Markov Fluid model(GMFM),introduced by Marr6n(2012),is assumed for the sources because it describes in a versatile way the traffic,allows estimation based on traffic traces,and also consistent effective bandwidth(EB)estimation can be done.QoS,interpreted as buffer overflow probability,can be estimated for GMFM through the EB estimation and solving the optimization problem presented in Courcoubetis and Siris[Perform.Eval.48:5-23,2002],the so-called inf-sup formulas.In this work,we implement a code to solve the inf-sup problem and other optimization related with it,that allow us to do traffic engineering in links of data networks to calculate both,minimum capacity required w hen QoS and buffer size are given or minimum buffer size required when QoS and capacity are given.