The Zero-Adjusted Log-Symmetric Distributions: Point and Intervalar Estimation.

In this paper, a new class of semi-continuous distributions called zero-adjusted log-symmetric is introduced and studied. Some properties and parameters estimation by maximum likelihood method are derived and confidence intervals (CIs) are developed. A simulation study is conducted to evaluate properties of the maximum likelihood estimators in lighter/heavier-tailed distributions. Finally, an application in a real data set is presented to illustrate the flexibility of the proposed class of distributions.

A Clustering Algorithm for Polygonal Data Applied to Scientific Journal Profiles.

Millions of papers are submitted and published every year, but researchers often do not have much information about the journals that interest them. In this paper, we introduced the first dynamical clustering algorithm for symbolic polygonal data and this was applied to build scientific journals profiles. Dynamic clustering algorithms are a family of iterative two-step relocation algorithms involving the construction of clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between clusters and their corresponding prototypes The application gives a powerful vision to understand the main variables that describe journals. Symbolic polygonal data can represent summarized extensive datasets taking into account variability. In addition, we developed cluster and partition interpretation indices for polygonal data that have the ability to extract insights about clustering results. From these indices, we discovered, e.g., that the number of difficult words in abstract is fundamental to building journal profiles.

Interval regression model adequacy checking and its application to estimate school dropout in Brazilian municipality educational scenario.

Interval-valued data have been commonly encountered in practice, and Symbolic Data Analysis provides a solution to the statistical treatment of these data. Regression analysis for interval-valued symbolic data is a topic that has been widely investigated in the literature of symbolic data analysis, and several models from different paradigms have been proposed. There are basic regression assumptions, and it is essential to validate them. This paper introduces an approach to check interval regression model adequacy based on residual analysis. Concepts of ordinary and standardized interval residual are presented, and graphical analysis of these residuals is also proposed. To show the usefulness of the proposed approach, an application for estimating school dropout in the scenario of Brazilian municipalities is performed. We observed some outliers from the interval residuals analysis, and interval robust regression models are more suitable for estimating school dropout.

Bivariate Birnbaum-Saunders accelerated lifetime model: estimation and diagnostic analysis.

In this paper, we discuss the bivariate Birnbaum-Saunders accelerated lifetime model, in which we have modeled the dependence structure of bivariate survival data through the use of frailty models. Specifically, we propose the bivariate model Birnbaum-Saunders with the following frailty distributions: gamma, positive stable and logarithmic series. We present a study of inference and diagnostic analysis for the proposed model, more concisely, are proposed a diagnostic analysis based in local influence and residual analysis to assess the fit model, as well as, to detect influential observations. In this regard, we derived the normal curvatures of local influence under different perturbation schemes and we performed some simulation studies for assessing the potential of residuals to detect misspecification in the systematic component, the presence in the stochastic component of the model and to detect outliers. Finally, we apply the methodology studied to real data set from recurrence in times of infections of 38 kidney patients using a portable dialysis machine, we analyzed these data considering independence within the pairs and using the bivariate Birnbaum-Saunders accelerated lifetime model, so that we could make a comparison and verify the importance of modeling dependence within the times of infection associated with the same patient.

Pelagic microplastics around an archipelago of the Equatorial Atlantic.

Plastic marine debris is presently widely recognised as an important environmental pollutant. Such debris is reported in every habitat of the oceans, from urban tourist beaches to remote islands and from the ocean surface to submarine canyons, and is found buried and deposited on sandy and cobble beaches. Plastic marine debris varies from micrometres to several metres in length and is potentially ingested by animals of every level of the marine food web. Here, we show that synthetic polymers are present in subsurface plankton samples around Saint Peter and Saint Paul Archipelago in the Equatorial Atlantic Ocean. To explain the distribution of microplastics around the Archipelago, we proposed a generalised linear model (GLM) that suggests the existence of an outward gradient of mean plastic-particle densities. Plastic items can be autochthonous or transported over large oceanic distances. One probable source is the small but persistent fishing fleet using the area.