Renata Rojas Guerra

A tripanossomíase é uma doença endêmica das Américas, presente em áreas urbanas e rurais. Os cães desempenham um papel fundamental na manutenção do Trypanosoma spp. e também podem desenvolver formas clínicas da doença. Entre as diferentes espécies conhecidas, T. evansi e T. cruzi são as mais comumente relatadas em cães. Este estudo utilizou o Teste Indireto de Anticorpos Fluorescentes (IFAT) para determinar e comparar a frequência de detecção de cães reativos para Trypanosoma spp. na região central do Rio Grande do Sul em 2021 e 2024. Um total de 700 amostras de soro de cães tratados no Hospital Veterinário da Universidade Federal de Santa Maria foram usadas, com 350 amostras coletadas em 2021 e 2024. Destes, 140/350 (40%) e 150/353 (42,9%) foram reativos para Trypanosoma spp. em 2021 e 2024, respectivamente. Esses achados destacam a importância de pesquisas epidemiológicas para monitorar a circulação de Trypanosoma spp. na população canina em diferentes regiões do Brasil, dado o papel dos cães como reservatório para espécies de Trypanosoma, incluindo T. cruzi, e a relevância clínica da infecção por T. cruzi em humanos.

This study aimed to evaluate the relationship between the age of domestic cats and the detection rate of antibodies against Toxoplasma gondii after the 2018 human toxoplasmosis outbreak in Santa Maria, Rio Grande do Sul, Brazil. Serum samples from 169 cats were analyzed by Indirect Immunofluorescence Assay (IFA) for IgG antibodies. Seropositivity was observed in 25.45% of the samples, with rates of 10.34% in kittens, 38.71% in young cats, 30.51% in adults, and 33.33% in seniors. A significant association was found between age and seropositivity (p = 0.02496). Young cats showed approximately 2.74 times higher risk of exposure compared to kittens. These results indicate that exposure to T. gondii increases with age, suggesting cumulative environmental and behavioral risk factors. The findings highlight the epidemiological importance of cats in maintaining the parasite’s life cycle and reinforce the need for preventive strategies to reduce environmental contamination and protect both animal and human health.

In this study, we introduce the gamma power generalized Weibull (GPGW) distribution and investigate several of its main mathematical properties. The performance of the maximum likelihood estimators is evaluated through Monte Carlo simulations. The practical relevance of the proposed distribution is illustrated through an application to real bibliometric data, where the GPGW is used to model SCImago Journal Rank (SJR) indicators. In comparison with alternative models commonly employed for lifetime and positive data, the GPGW distribution exhibits strong competitive performance. In particular, in the real data application, it outperforms eleven competing distributions in terms of goodness of fit criteria, including the power generalized Weibull (PGW), the gamma-Nadarajah–Haghighi (GNH), and the exponentiated power generalized Weibull (EPGW) distributions. While inheriting several mathematical features of the EPGW distribution, such as expressions for moments, skewness, and kurtosis, the GPGW offers enhanced flexibility, making it a valuable modeling tool for lifetime data and heavy-tailed positive measurements.

This paper introduces a new time series model based on the reflected unit Burr XII (RUBXII) distribution that is an alternative to the Kumaraswamy autoregressive moving average and Beta autoregressive moving average models for time series analysis taking values in the standard unit interval. The proposed model describes the conditional median of RUBXII-distributed discrete-time series by a dynamic structure that includes autoregressive and moving average (ARMA) terms, a set of regressors, and a link function. We perform the model’s parameter estimation using the conditional maximum likelihood method. Closed-form expressions for the score vector and observed information matrix are presented. We propose and discuss techniques of diagnostic and forecasting for the new model. A Monte Carlo simulation study is carried out to evaluate the finite sample performance of the conditional maximum likelihood estimator. Finally, the proportion of stored hydroelectric energy in Northern Brazil is analyzed through the proposed model. The results evidence that the introduced RUBXII-ARMA model is suitable for describing the dynamics of the data and provides more accurate forecasts for the proportion of stored energy in Northern Brazil than those from competitors’ models.

The Unit Gompertz (UGo) distribution has two parameters and is adequate for modeling data with support in the interval (0,1). It was introduced as an alternative to the beta and Kumaraswamy distribution for modeling double-bounded variables. The UGo distribution can accommodate asymmetric data and has been applied in various situations, such as environmental studies, industrial applications, and survival analysis. An attractive characteristic of the UGo is its closed-form expression for the quantile function. It allows for the formulation of a quantile-based parameterization and accommodates different dependence structures for modeling the conditional quantiles. Therefore, in this study, we introduce a simple alternative for modeling double-bounded variables under serial correlation in a conditional quantile of the UGo distribution. The so-called UGo-ARMA is constructed considering an autoregressive moving average structure using the UGo distribution as the random component. The maximum likelihood method was used for parameter estimation. Subsequently, Monte Carlo simulations are conducted to investigate the performance of the maximum likelihood estimators and the asymptotic confidence intervals of the parameters. The proposed model is an alternative for modeling double-bounded variables with serial correlation, especially in contexts where UGo has already proven competitive with classic unitary distributions. To illustrate the practical relevance of the proposed model, we apply it to a financial time series: the average monthly interest rate for credit card installment operations in Brazil. The results highlight the model’s ability to capture serial dependence and distributional features typically found in real-world bounded data.

The exponentiated transmuted-G family was introduced along with some of its statistical properties derived from a power series expansion. This work demonstrates that the previously proposed expansions, which depend on a double sum, present convergence problems for some combinations of parameters. Therefore, a much simpler linear representation that depends only on a single sum is presented, allowing for the precise and general calculation of these properties.

The Sustainable Development Goals (SDGs) of the United Nations consist of 17 general objectives, subdivided into 169 targets to be achieved by 2030. Several SDG indices and indicators require continuous analysis and evaluation, and most of these indices are supported in the unit interval (0,1). To incorporate the flexibility of the modified Weibull (MW) distribution in doubly constrained datasets, the first objective of this work is to propose a new unit probability distribution based on the MW distribution. For this, a transformation of the MW distribution is applied, through which the unit modified Weibull (UMW) distribution is obtained. The second objective of this work is to introduce a quantile regression model for random variables with UMW distribution, reparameterized in terms of the quantiles of the distribution. Maximum likelihood estimators (MLEs) are used to estimate the model parameters. Monte Carlo simulations are performed to evaluate the MLE properties of the model parameters in finite sample sizes. The introduced methods are used for modeling some sustainability indicators related to the SDGs, also considering the reading skills of dyslexic children, which are indirectly associated with SDG 4 (Quality Education) and SDG 3 (Health and Well-Being).

This study analyzes the factors influencing the proportions of blank and null votes in Brazilian municipalities during the 2018 presidential elections. The behavior of the variable of interest is examined using unit regression models within the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework. Specifically, five different unit regression models are explored, beta, simplex, Kumaraswamy, unit Weibull, and reflected unit Burr XII regressions, each incorporating submodels for both indexed distribution parameters. The beta regression model emerges as the best fit through rigorous model selection and diagnostic procedures. The findings reveal that the disaggregated municipal human development index (MHDI), particularly its income, longevity, and education dimensions, along with the municipality’s geographic region, significantly affect voting behavior. Notably, higher income and longevity values are linked to greater proportions of blank and null votes, whereas the educational level exhibits a negative relationship with the variable of interest. Additionally, municipalities in the Southeast region tend to have higher average proportions of blank and null votes. In terms of variability, the ability of a municipality’s population to acquire goods and services is shown to negatively influence the dispersion of vote proportions, while municipalities in the Northeast, North, and Southeast regions exhibit distinct patterns of variation compared to other regions. These results provide valuable insights into electoral participation’s socioeconomic and regional determinants, contributing to broader discussions on political engagement and democratic representation in Brazil.

This research aims to propose a machine learning approach to classify dropout outcomes among students in Statistics undergraduate programs in Brazil, identifying the most important factors associated with this phenomenon. This study uses microdata from the National Institute for Educational Studies and Research Anísio Teixeira (INEP) Higher Education Census to analyze the experiences of 10,387 undergraduate students who began their bachelor’s degrees between 2009 and 2014 in Brazil and were monitored until the end of 2017. Approximately 65% of these students dropped out, highlighting the critical dropout challenge in Brazilian Statistics programs. The outcome of interest is the student’s final status in the census data, either completion or dropout. Logistic regression, random forest , and support vector machine algorithms were fitted in the learning stage. For each algorithm, the best model was evaluated on test data. Results indicate that the logistic regression model outperforms the others, achieving an accuracy of 86.1% and offering greater interpretability compared to the other models. The study reveals that the duration of enrollment is a key predictor of dropout likelihood. Moreover, receiving social support and participating in complementary activities are significant factors in reducing dropout rates.