Regression models to dependence for exceedance

Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.     

A Bayesian model for multiple change point to extremes,with application to environmental and financial data

Abrupt changes often occur for environmental and financial time series. Most often, these changes are due to human intervention. Change point analysis is a statistical tool used to analyze sudden changes in observations along the time series. In this paper, we propose a Bayesian model for extreme values for environmental and economic datasets that present a typical change point behavior. The model proposed in this paper addresses the situation in which more than one change point can occur in a time series. By analyzing maxima, the distribution of each regime is a generalized extreme value distribution. In this model, the change points are unknown and considered parameters to be estimated. Simulations of extremes with two change points showed that the proposed algorithm can recover the true values of the parameters, in addition to detecting the true change points in different configurations. Also, the number of change points was a problem to be considered, and the Bayesian estimation can correctly identify the correct number of change points for each application. Environmental and financial data were analyzed and results showed the importance of considering the change point in the data and revealed that this change of regime brought about an increase in the return levels, increasing the number of floods in cities around the rivers. Stock market levels showed the necessity of a model with three different regimes.     

A semiparametric Bayesian approach to extreme value estimation

This paper is concerned with extreme value density estimation. The generalized Pareto distribution (GPD) beyond a given threshold is combined with a nonparametric estimation approach below the threshold. This semiparametric setup is shown to generalize a few existing approaches and enables density estimation over the complete sample space. Estimation is performed via the Bayesian paradigm, which helps identify model components. Estimation of all model parameters, including the threshold and higher quantiles, and prediction for future observations is provided. Simulation studies suggest a few useful guidelines to evaluate the relevance of the proposed procedures. They also provide empirical evidence about the improvement of the proposed methodology over existing approaches. Models are then applied to environmental data sets. The paper is concluded with a few directions for future work.     

Urban or rural fragments: which soil use in forest surroundings induces greater litter deposition?

Urban Ecosystems - The soil use in surroundings of forest remnants, as well as, variations in seasonality and rainfall totals from each year might influence the composition, structure, and...     

Power and identity: the exhibition of human beings in the Portuguese great exhibitions

This article reflects on the inclusion of human beings in the colonial representations of the great exhibitions that Portugal organised or took part in during the first half of the twentieth century. It analyses the role played by the natives (from the Portuguese colonies), as well as the way they were represented and treated, based on various documents and interviews and on the study of the exhibition creation process. These exhibitions revealed some underlying tensions. On the one hand, they provided evidence of the differences between the ‘civilised’ and the ‘uncivilised’, of the diversity of ‘races’ and of their places in a hierarchy of civilisation. On the other, they extolled the way colonised peoples adopted Portuguese models. The ways those human beings have asserted their existence, under the power of the exhibition’s organisers, provides a means to understand how they forged and assumed their identities in a context of rules.     

COVID Health Structure Index: The Vulnerability of Brazilian Microregions

Social Indicators Research - Many developing countries have highly unequal health systems across their regions. The pandemic of COVID-19 brought an additional challenge, as hospital structures...     

Linking Human Development and the Financial Responsibility of Regions: Combined Index Proposals Using Methods from Data Envelopment Analysis

Social Indicators Research - Several indicators on human development and capabilities have been introduced in recent decades that measure the absolute level of deprivations and freedoms of people....     

Regression models for time-varying extremes

A common approach to modelling extreme data are to consider the distribution of the exceedance value over a high threshold. This approach is based on the distribution of excess, which follows the generalized Pareto distribution (GPD) and has shown to be adequate for this type of situation. As with all data involving analysis in time, excesses above a threshold may also vary and suffer from the influence of covariates. Thus, the GPD distribution can be modelled by entering the presence of these factors. This paper presents a new model for extreme values, where GPD parameters are written on the basis of a dynamic regression model. The estimation of the model parameters is made under the Bayesian paradigm, with sampling points via MCMC. As with environmental data, behaviour data are related to other factors such as time and covariates such as latitude and distance from the sea. Simulation studies have shown the efficiency and identifiability of the model, and applying real rain data from the state of Piaui, Brazil, shows the advantage in predicting and interpreting the model against other similar models proposed in the literature.     

Adoption of technology by the low-income population segment: The low-cost hot water heater case

The employment of means for efficient use of water and energy are being increasingly incentivized, in relation to the sustainability of buildings. In Brazil, there is much concern regarding the difficulty of getting low-income segments of the population to adopt energy-saving technologies. This study provides guidelines for the inclusion of manufactured, low-cost solar water heaters (LCSWH) in low-income housing in combination with environmental education of this segment of the population. Therefore, a group of dwellings located in a low-income community in Campinas SP, Brazil, was selected for installation of LCSWHs, which were assembled and installed by the study population itself, who also participated in environmental awareness workshops. Particularly apparent in the results obtained, was a tendency to reduce energy consumption by using the LCSWH, attributable in part to behavioral changes from environmental awareness; and that the planning, application or organized systematization of knowledge is feasible in low-income housing, provided it is not promoted solely by dependency culture, since the objective is to provide an instrument for self-discovery, mobilization and participation by the population.