Characterization of count data distributions involving additivity and binomial subsampling

In this paper we characterize all the $r$-parameter families of count distributions (satisfying mild conditions) that are closed under addition and under binomial subsampling. Surprisingly, few families satisfy both properties and the resulting models consist of the $r$th-order univariate Hermite distributions. Among these, we find the Poisson ($r=1$) and the ordinary Hermite distributions ($r=2$).Comment: Published at http://dx.doi.org/10.3150/07-BEJ6021 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Testing Universality in Critical Exponents: the Case of Rainfall

One of the key clues to consider rainfall as a self-organized critical phenomenon is the existence of power-law distributions for rain-event sizes. We have studied the problem of universality in the exponents of these distributions by means of a suitable statistic whose distribution is inferred by several variations of a permutational test. In contrast to more common approaches, our procedure does not suffer from the difficulties of multiple testing and does not require the precise knowledge of the uncertainties associated to the power-law exponents. When applied to seven sites monitored by the Atmospheric Radiation Measurement Program the test lead to the rejection of the universality hypothesis, despite the fact that the exponents are rather close to each other

Goodness of fit tests for the skew-Laplace distribution

The skew-Laplace distribution is frequently used to fit the logarithm of particle sizes and it is also used in Economics, Engineering, Finance and Biology. We show the Anderson-Darling and Cramér-von Mises goodness of fit tests for this distribution

Correction : Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health

Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero). This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural). The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package (https://cran.r-project.org/package=bayesZIB)

Analysis of zero inflated dichotomous variables from a Bayesian perspective : application to occupational health

Background: Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero). Methods: This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural). Results: The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. Conclusions: The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package (https://cran.r-project.org/package=bayesZIB)

Some mechanisms leading to underdispersion : Old and new proposals

Altres ajuts: acords transformatius de la UABIn statistical modeling, it is important to know the mechanisms that cause underdispersion. Several mechanisms that lead to underdispersed count distributions are revisited from new perspectives, and new ones are introduced. These include procedures based on the number of arrivals in arrival processes, such as renewal and pure birth processes and steady-state distributions of birth-death processes, like queues with state-dependent service rates. Weighted Poisson and other well-known underdispersed distributions are also related to birth-death processes. Classical and variable binomial thinning mechanisms are also viewed as important procedures for generating underdispersed distributions, which can also generate bivariate count distributions with negative correlation. Some example applications are shown, one of which is related to Biodosimetry

Estimation of wall shear stress using 4D flow cardiovascular MRI and computational fluid dynamics

Electronic version of an article published as Journal of mechanics in medicine and biology, 0, 1750046 (2016), 16 pages. DOI:10.1142/S0219519417500464 © World Scientific Publishing CompanyIn the last few years, wall shear stress (WSS) has arisen as a new diagnostic indicator in patients with arterial disease. There is a substantial evidence that the WSS plays a significant role, together with hemodynamic indicators, in initiation and progression of the vascular diseases. Estimation of WSS values, therefore, may be of clinical significance and the methods employed for its measurement are crucial for clinical community. Recently, four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) has been widely used in a number of applications for visualization and quantification of blood flow, and although the sensitivity to blood flow measurement has increased, it is not yet able to provide an accurate three-dimensional (3D) WSS distribution. The aim of this work is to evaluate the aortic blood flow features and the associated WSS by the combination of 4D flow cardiovascular magnetic resonance (4D CMR) and computational fluid dynamics technique. In particular, in this work, we used the 4D CMR to obtain the spatial domain and the boundary conditions needed to estimate the WSS within the entire thoracic aorta using computational fluid dynamics. Similar WSS distributions were found for cases simulated. A sensitivity analysis was done to check the accuracy of the method. 4D CMR begins to be a reliable tool to estimate the WSS within the entire thoracic aorta using computational fluid dynamics. The combination of both techniques may provide the ideal tool to help tackle these and other problems related to wall shear estimation.Peer ReviewedPostprint (author's final draft

Probability estimation of a Carrington-like geomagnetic storm

Intense geomagnetic storms can cause severe damage to electrical systems and communications. This work proposes a counting process with Weibull inter-occurrence times in order to estimate the probability of extreme geomagnetic events. It is found that the scale parameter of the inter-occurrence time distribution grows exponentially with the absolute value of the intensity threshold defining the storm, whereas the shape parameter keeps rather constant. The model is able to forecast the probability of occurrence of an event for a given intensity threshold; in particular, the probability of occurrence on the next decade of an extreme event of a magnitude comparable or larger than the well-known Carrington event of 1859 is explored, and estimated to be between 0.46% and 1.88% (with a 95% confidence), a much lower value than those reported in the existing literature

Ultra log-concavity of discrete order statistics

Altres ajuts: acords transformatius de la UABIn this work we show that discrete order statistics preserve log-concavity and ultra log-concavity. We use a recursive expression for discrete order statistics and the concept of synchronized sequences. This finding allows to conclude that Poisson order statistics are underdispersed

A New Model of Biodosimetry to Integrate Low and High Doses

Biological dosimetry, that is the estimation of the dose of an exposure to ionizing radiation by a biological parameter, is a very important tool in cases of radiation accidents. The score of dicentric chromosomes, considered to be the most accurate method for biological dosimetry, for low LET radiation and up to 5 Gy, fits very well to a linear-quadratic model of dose-effect curve assuming the Poisson distribution. The accuracy of this estimation raises difficulties for doses over 5 Gy,the highest dose of the majority of dose-effect curves used in biological dosimetry. At doses over 5 Gy most cells show difficulties in reaching mitosis and cannot be used to score dicentric chromosomes. In the present study with the treatment of lymphocyte cultures with caffeine and the standardization of the culture time, metaphases for doses up to 25 Gy have been analyzed. Here we present a new model for biological dosimetry, which includes a Gompertz-type function as the dose response, and also takes into account the underdispersion of aberrationamong-cell distribution. The new model allows the estimation of doses of exposures to ionizing radiation of up to 25 Gy. Moreover, the model is more effective in estimating whole and partial body exposures than the classical method based on linear and linear-quadratic functions, suggesting their effectiveness and great potential to be used after high dose exposures of radiation